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User Personality and the New User Problem in a Context-Aware Point of Interest Recommender System



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User Personality and the New User Problem in a Context-
Aware Points of Interest Recommender System
Matthias Braunhofer,
Mehdi Elahi, and
Francesco Ricci
Faculty of Computer Science
Free University of Bozen - Bolzano, Italy,,
The new user problem is an important and challenging issue that Context-Aware Recommender
Systems (CARSs) must deal with, especially in the early stage of their deployment. It occurs
when a new user is added to the system and there is not enough information about the user’s
preferences in order to compute appropriate recommendations. It is common to address this
problem in the recommendation algorithm, by using demographic attributes such as age,
gender, and occupation, which are easy to collect and are reasonably good predictors of the user
preferences. However, as we show here, user’s personality provides even better information for
generating context-aware recommendations for places of interest (POI), and it is still easy to
assess with a simple questionnaire. In our study, using a rating data set collected by a mobile
app called STS (South Tyrol Suggests), we have found that by considering the user personality
the system can better rank the recommendations for the new users.
Keywords: Context-Aware Recommender Systems; Cold-Start Problem; New User Problem;
Personality; Demographics
1 Introduction
With the tremendous growth of information available on the World Wide Web, it has
become more and more difficult to search for and find relevant information on the
Web. For instance, users of online tourism portals often find it difficult to select a
hotel or a place to stay because of the overwhelming number of travel offers and the
lack of effective system support. Recommender Systems (RSs) help overcome this
information overload by providing users with selected (information) items that match
their personal preferences (Ricci et al., 2011). The suggested items can be obtained by
comparing the user’s profile (which appropriately models the user preferences) with
the descriptions of items (content-based approach) or with the profiles of other users
(collaborative-filtering approach).
Context-Aware Recommender Systems (CARSs) are a special type of RSs that
generate more relevant recommendations by adapting them to the specific contextual
situation of the user (e.g., weather, temperature, season, and companion). Over the
last couple of years, increasingly more elaborate recommendation algorithms and
techniques for incorporating contextual information into the recommendation process
have been proposed. According to (Adomavicius et al., 2011), they can be classified
as contextual pre-filtering (or contextualisation of the recommendation input),
contextual post-filtering (or contextualisation of the recommendation output) and
contextual modelling (or contextualisation of the recommendation function),
depending on the computational stage where the available contextual information is
Nevertheless, the cold-start problem remains a demanding challenge for CARSs as
well. This problem occurs when: 1) a new user without rating history requests a
recommendation from the system (new user problem); 2) the RS is asked to generate
a recommendation for an unrated item (new item problem); or 3) the RS is requested
for recommendations under contextual situations that have not been rated before (new
context problem).
This paper focuses on the new user problem, which from the user’s point of view is
the most important one as it prevents new users from obtaining any recommendation
tailored to their interests and needs. A common approach to this problem is either to
provide non-personalised recommendations (e.g. popular items) or to utilize simpler
recommendation techniques, for instance, those based on demographic user data, such
as gender, age group, Zip code and income level. For instance, based on the
knowledge that males like soccer and females like figure skating”, information
items related to soccer and figure skating can be suggested to users with those
demographics, respectively.
In this paper, we propose to consider the users’ personality to overcome the new user
problem in CARSs. We model the personality with the five-factor model (FFM) or
Big-5 personality traits (Costa & McCrae, 1996), which is currently the most
widespread and widely accepted approach. According to this model, user personality
is measured along five dimensions: openness, conscientiousness, extraversion,
agreeableness, and neuroticism. The main reason for using personality in RSs is that it
is a predictable and stable factor that explains human behaviours. It has been shown
that there exist direct links between personality and interests / preferences (Rentfrow
et al., 2003), and hence, people with similar personality traits have similar interests /
preferences. Moreover, user personality can be acquired through self-report
questionnaires, such as the Five-Item Personality Inventory (FIPI) (Gosling et al.,
2003), which we have used, or the 120 or 240 item International Personality Item
Pool Representation of the NEO PI-R (IPIP-NEO) (Goldberg et al., 2006).
In a previous paper (Elahi et al., 2013) we have used the personality not only to
generate better recommendations, but also to identify which ratings to acquire from
the user, i.e., which POIs are useful for users to rate because they better describe the
user preferences. The proposed rating elicitation technique leads to a significant
increase in the number of ratings obtained from the users as well as the improvement
of the recommendation accuracy. In this paper we further develop that analysis by
comparing the benefit of exploiting the user personality to the usage of demographic
information of the user, which is a more common approach. We investigate the effect
of each of the five considered personality factors separately in order to determine
which of them largely contribute to the accuracy of the recommendation model. To
the best of knowledge, this is a novel work especially in the tourism domain.
In summary, in this article we present the evaluation of the usage of the Big-5
personality traits (Costa & McCrae, 1996) in a state-of-the-art CARS algorithm, i.e.,
CAMF-CC (Baltrunas et al., 2011), and we compare it to a similar model that uses
demographic attributes (i.e., age group and gender). Experiments were executed using
the rating dataset derived from the South Tyrol Suggests (STS) app (Braunhofer et al.,
2014a), an Android-based CARS that provides users with place of interest (POI)
recommendations using various contextual factors (e.g., weather, time of day,
location, and mood). The results show that the ranking quality and the prediction
accuracy of CARS can be improved by exploiting personality characteristics that cut
across demographic categories.
The rest of this paper is organized as follows. Section 2 reviews the state of the art. In
Section 3, we explain the implemented procedure to collect the demographics and
user personality in our STS app. In Section 4, we present the experimental evaluation
and the obtained results. Finally, in Section 5 we elaborate conclusions and give
future work directions.
2 Related Work
Research on CARSs is a rather fresh topic that has drawn considerable attention in the
last years. These systems recommend items personalised for each user by considering
the contextual situation of the item and the user (Adomavicius et al., 2011). For
instance, liveCities (Martin et al., 2011) is a CARS that, by considering the location of
the tourists, sends them push-based notifications such as a suggestions about what to
do, or discounts offered by nearby restaurants. In fact, various CARSs exploits the
contextual information of the tourists to provide them with more relevant suggestions
(Costa et al., 2013; Meehan et al., 2013; Barranco et al., 2012).
While recommendation techniques exploited in CARSs can be effective and robust
for their frequent users, all of them have poor performance for new users and new
items (cold-start problem). Particularly, in collaborative filtering based recommender
systems, which are the type of recommenders that we are considering in this work, the
cold-start problem is determined by the lack of a sufficient number of ratings for the
system to be able to compute relevant and diverse suggestions.
This problem is even worse for CARSs. This is because, in order to properly function,
these systems must collect not only ratings given by users to items, but also the
description of the contextual situation under which the items were experienced by the
users and that can have influenced the users appreciation of the items. For instance,
consider a CARS that has obtained many low ratings for a mountain hiking route
when the weather context of the user experience of the route was “rainy” (which here
we assume that influenced negatively the ratings). However, suppose that the system
has never collected any rating when the weather context was “sunny”, which is a
situation that we assume here can be more suited for that experience. In such a case,
that route would not be recommended to the users in a sunny day since the system has
no knowledge of how the users rate that route in a sunny day and it is not able to
make a correct extrapolation from the low ratings given in the “rainy” contextual
A common solution of the cold-start problem in RSs, relies on the usage of
demographic information of the users. For example, (Pazzani, 1999) computed the
user-to-user similarity in a neighbor-based collaborative filtering system by using
demographic attributes of the users, e.g., gender, age group, area code, education, or
employment. This is an example of a more general class of techniques that is
sometimes called Demographic Filtering (Adomavicius et al, 2005), which is often
used in tourism applications. For example, (Wang et al, 2012) applies demographic
filtering to categorize the tourists by their demographic attributes and generate
recommendations based on their demographic classes. The main advantage of such
systems is that they do not require any rating or extra knowledge from the users to
make recommendations for them.
A more novel line of solutions of the cold-start problem is based on the exploitation
of personality information of the users. As mentioned before, personality is a user
characteristic that influences very much decision making as well as tastes and
interests (Rentfrow et al., 2003; John, 1999). In fact, people with similar personality
usually share similar interests and tastes.
In order to be able to incorporate personality information of the users in the
recommendation process, the system must identify and quantify the personality of the
user. This can be done either explicitly, by asking the user to complete a personality
questionnaire, or implicitly, by observing users behavioral patterns during the
interaction with the system (Kosinski et al., 2013). It has been shown that explicit
personality acquisition has higher accuracy and yields to better results in terms of
various measures such as user satisfaction, ease of use, and prediction accuracy (Dunn
et al., 2009). Moreover, explicit acquisition of user personality is easy. In (Hu et al.,
2009) it has been shown that the users spent significantly less effort, in terms of
perceived cognitive or actual task easiness and time, to complete the preference
elicitation stage in a personality based recommender system than in a rating-based
recommender system. In other words, it is also easier for the user to reply to questions
aimed at scoring the users in a personality model rather than evaluating items in given
rating scale (e.g., five star). Users have also expressed a stronger intention for re-
using the personality-based system. Hence, using personality characteristics of the
user can be a valuable source of information for the recommender systems for better
recommendation generation (Hu et al., 2011; Hu et al., 2009; Tkalcic et al., 2013).
3 South Tyrol Suggests (STS)
South Tyrol Suggests (STS1) is an Android-based mobile application that provides
tourists with recommendations for approximately 27,000 POIs (i.e., accommodations,
attractions, events, public services and restaurants) in South Tyrol (Italy) by
exploiting various contextual factors (e.g., weather, time of day, day of week,
location, and mood) in a recommendation algorithm that is based on matrix
factorisation (Koren & Bell, 2011). In particular, in STS, we have extended the
CAMF-CC model proposed by Baltrunas et al. (2011). This is a context-aware matrix
factorisation approach that integrates additional parameters for each contextual
condition and item category combination, besides the standard parameters used in
context-free matrix factorisation. We enhanced the original CAMF-CC model by
incorporating also demographics and/or personality information of the users, which
allows gathering behavioural information regardless of the availability of explicit
1 South Tyrol Suggests (STS):
ratings from the user. This approach follows an analogous solution described in
(Koren & Volinsky, 2009). Given a user u, an item i, a contextual situation described
by the contextual conditions c1,…,ck and the set of attributes T(i) associated to item i,
it predicts ratings using the following formula:
where qi, pu and ya are the latent factor vectors representing the item i, the user u and
the user attribute a (i.e., a particular gender, age group or Big-5 personality trait
score), respectively. ī is the average rating for item i, bu is the bias associated to user
u, and btcj is a parameter that models how the contextual condition cj influences the
rating of items belonging to category t. It takes a positive (negative) value depending
on whether the contextual condition cj has a positive (negative) effect on the ratings
for items belonging to category t. This allows, for instance, recommending indoor
POIs (e.g., museums, churches, castles) on bad weather conditions and outdoor POIs
(e.g., lakes, mountain hikes, scenic walks) on good weather conditions.
The model parameters are learned offline, once every five minutes, by minimising the
associated regularised squared error function through stochastic gradient descent
(Koren & Bell, 2011). This learning procedure is fast; it takes only a few seconds to
re-train the prediction model when new ratings are acquired.
3.1 Demographic Information
During the registration process STS asks to its users the following demographic data
(as can be seen in Figure 1, left): gender and birthday. Gender is given in a nominal
scale (female/male) and can be easily integrated into the extended matrix factorization
model of STS (as shown in Equation 1), in order to enhance the user representation.
Birthday, on the other hand, is an ordinal variable and we converted it to a less fine-
grained scale, just using 6 levels: <18, 18-25, 26-39, 40-54, 55-69, and 70+. These
age groups can then be used as additional user attributes integrated in the rating
prediction factor model, analogously to the gender attribute.
To better illustrate this, let us assume that the prediction model is asked to predict the
rating a female user u in the 18-25 age group would give to the item i, given that the
contextual situation c1,…,ck holds. In this case, the rating prediction formula is
rewritten as follows:
!𝑝!+𝑦!"#$%" +𝑦!"!!" .
Here, user u is now characterized by 3 components: the latent factor vector pu (which
is learned based on her prior ratings, if any), the latent factor vector yfemale (which is
learned from prior ratings given by female users) and the latent factor vector y18-25
(which is learned from prior ratings of users that are between 18 and 25 years old).
3.2 Personality Information
As part of the registration process, users are also asked to fill out the Five-Item
Personality Inventory (FIPI) (Gosling et al., 2003) so that the system can assess their
Big-5 personality traits, i.e., openness to experience, conscientiousness, extraversion,
agreeableness and neuroticism. Figure 1 (middle) shows a screenshot of STS where
one of the questionnaire statements (i.e., I see myself as open to experience,
imaginative.”) is displayed. The full FIPI questionnaire takes approximately one
minute to complete, and consists of the following five statements to be rated on a 7-
point Likert scale from “strongly disagree” to “strongly agree”:
1. I see myself as open to experience, imaginative;
2. I see myself as dependable, organized;
3. I see myself as extraverted, enthusiastic;
4. I see myself as agreeable, kind;
5. I see myself as emotionally stable, calm.
The score for a specific personality trait is calculated by dividing the score (e.g., 0 for
“strongly disagree”, 1 for “disagree moderately”, …, 6 for “strongly agree”) by 6 (i.e.,
the maximum score), which gives a result in the interval [0, 1]. This result is then
discretized into 5 categories (i.e., very high, high, neutral, low, very low”)
so that it can be integrated into the rating prediction model of STS, in order enhance
the user representation as well.
To see how this works, let us again consider an imaginary user u that scored high on
extraversion (= ext) and low on openness to experience (= ope), conscientiousness (=
Fig. 1. Sample screenshots of STS
con), agreeableness (= agr) and neuroticism (= neu). Then, her rating prediction
formula for item i and contextual situation c1,…,ck becomes as follows:
!𝑝!+𝑦!"#_!"# +𝑦!"#_!"# +𝑦!"#_!!"!+𝑦!"#_!"# +𝑦!"#_!"# .
Now, user u is profiled by 6 components: the latent factor vector pu and the latent
factor vectors yope_low, ycon_low, yext_high, yagr_low and yneu_low, which are derived from the
prior ratings of users that scored high on extraversion and low on openness,
conscientiousness, agreeableness and neuroticism, respectively.
4 Evaluation Methodology
In order to compare the effectiveness of the context-aware recommendation model of
STS when it is using the demographic information vs. the personality information, we
performed an offline experiment. The goal was two-fold: (1) to compare the
recommendation accuracy for new users that is achieved by using the demographics
information vs. that achieved by using the Big-5 personality traits; and (2) to identify
the demographic attributes or the Big-5 personality traits that, if introduced in the
prediction model, cause it to generate more accurate recommendations for new users.
4.1 Dataset
The rating dataset that we have used in the experiments was collected in about one
year of activity of our STS app. In total, the STS dataset contains 2,534 ratings
expressed on an ordinal scale from 1 (= “I didn’t like it”) to 5 (= “I liked it very
much”), with the steps of 1. Many ratings (i.e., 64.17 %) contained in this dataset are
augmented with the information about the contextual situations (at the time the items
were experienced) described by up to 14 contextual conditions (e.g., weather,
temperature, season, daytime, companion), which could be specified by the user by
means of an appropriate graphical user interface within the application, as shown in
Figure 1 (right). Moreover, the STS dataset contains basic demographics (i.e., age
group and gender) and personality information (i.e., Big-5 personality trait scores) of
the users.
Some of the ratings contained in the STS dataset were entered by users for which no
demographic and personality information was available. This happened when users
did not enter birthday/gender data on the registration screen or skipped the personality
questionnaire. Since in the evaluation, we wanted to compare the effect of using
personality information vs. using demographics in the CAMF model, we considered
only ratings from users for which both demographics and personality information
were available. In this way we obtained a dataset of 1,379 ratings, whose details are
summarized in Table 1.
Table 1. STS dataset information
Total number of ratings
Number of users
Number of items
Number of contextual factors
Number of contextual conditions
Number of contextual situations
Number of demographic attributes
Number of personality attributes
4.2 Evaluation Procedure
In order to compare the effectiveness of the system to produce recommendations for
new users in the two mentioned conditions, we carried out a ten-fold cross-validation
scheme as in (Braunhofer et al., 2014b, Shani et al., 2008). First, we randomly split
the users in the entire rating dataset into ten mutually exclusive subsets of
approximately equal size. Then, in each cross-validation run, we used the ratings
coming from one of the obtained user splits as testing set and the remaining ones as
training set to train the several distinct variants of the CAMF-CC model. In this
manner, we created a test set of ratings coming from users that have no ratings in the
training set, i.e., really cold (new) users without any observed rating. For each of the
ten iterations, two performance metrics were recorded:
1. Mean Absolute Error (MAE) (Shani & Gunawardana, 2011): This metric
evaluates the accuracy of predicted ratings. This is achieved by predicting
the rating 𝑟
!"!!!! for each user-item-context triple (u, i, c1…ck) in the test set
T and comparing it with the actual rating 𝑟
!"!!!!, as it is done in the
following formula:
2. Normalized Discounted Cumulative Gain (nDCG) (Shani & Gunawardana,
2011): nDCG is a measure used to determine the effectiveness of RSs in
correctly ranking the recommendations compared with an optimal ranking.
Both, nDCG and MAE, were calculated based on the commonly accepted test
procedure proposed by Park & Chu (2009), which attempts to avoid performance bias
that is caused by the overrepresentation of high raters. It does so by clustering items
based on the ratings given by each user, and then randomly sampling one item for
each cluster to be used as test item. MAE and nDCG1 were then calculated on the
basis of these test items. We also calculated nDCGk, with k = 2, . . . 5, but for lack of
space and since they gave similar results we omit them.
Finally, the obtained MAE and nDCG estimates were averaged over all 10 runs, to
produce cross-validation estimates of MAE and nDCG.
4.3 Evaluation Results
The obtained results are shown in Table 2. As can be noted, profiling new users
through personality information led to more accurate recommendations, compared to
profiling them by demographics. Overall, the context-aware rating prediction model
achieved an MAE of 0.968 (SD = 0.079) by using the users’ Big-5 personality trait
information, whereas it achieved an MAE of 0.970 (SD = 0.081) by relying on
demographics (the lower the better). Similar results were obtained in terms of nDCG;
nDCG was higher by using personality than by using demographics (M = 0.723, SD =
0.055 vs. M = 0.665, SD = 0.065) (the higher the better). Subsequent t-tests performed
on both MAE and nDCG differences between models revealed that the MAE
difference was not significant (p = 0.452), whereas it was statistically significant the
difference of nDCG (p = 0.001). We believe that with a larger rating dataset also the
difference in MAE will likely become statistically significant. Hence, these
observations support our hypothesis that recommendations for new users are more
effective when they are generated based on the user’s personality type.
Table 2. Evaluation results
Total MAE
Total nDCG
Age group
Emotional stability
Openness to experience
Moreover, Table 2 shows a comparison of the MAE and nDCG obtained by
enhancing the user representation in the context-aware rating prediction model with
individual demographic attributes (i.e., gender and age group) and personality traits
(i.e., extraversion, agreeableness, conscientiousness, emotional stability and openness
to experience). It can be seen that the best recommendation performance in terms of
both MAE (M = 0.959, SD = 0.092) and nDCG (M = 0.704, SD = 0.046) is achieved
using the emotional stability factor. This shows that emotional stability can be a
significant predictor for the user’s POI preferences, which is in line with previous
findings that showed emotional stability to be a good predictor of media preferences
(Hertel et al., 2008) and music preferences (Rentfrow & Gosling, 2006).
To sum up, the obtained results seem encouraging as they suggest that adapting the
recommendations to the target user’s personality profile can increase the system
recommendation effectiveness.
5 Discussions and Future Works
In this paper we have illustrated the benefits of incorporating the user personality
information in order to tackle the cold-start problem in a mobile context aware
recommender system for tourism called South Tyrol Suggests. We have analysed the
impact of utilizing personality traits, either individually or together, on the quality of
the recommendations, measured with well-known quality metrics, i.e., MAE
(indicator of rating prediction error) and nDCG (indicator of ranking quality). We
have shown that utilizing personality information is more effective than utilizing
demographic information of users, which is a more common approach to tackle the
cold-start problem. Moreover, we have shown that utilizing even a single trait out of
the five personality traits can still result in a significant improvement of the
recommendation quality. This last result implies that the personality traits are not
equally informative of the users long-term behavioural characteristics. Hence,
identifying the more important traits, that better represent and model the users’
characteristics, can be beneficial in designing and implementing better and easier to
apply personality based recommendation algorithms.
There are several research directions that we can consider for future work. First, we
are interested in analysing the usage of mobile phones and smart gadgets, such as
smart bracelets, in order to predict the user personality. In fact, it has been shown that
there are relations between people’s personality traits and the usage behaviours they
exhibit, which can be monitored by the above-mentioned devices. For example,
(McAffee et al., 2011) showed that extravert people access more the texting
functionality of the smartphones while users with higher agreeableness exploit more
the calling functionality. Therefore, using the usage data collected by smart gadgets
(e.g., how, when, where the gadget is used), one can predict the user personality and
then, as shown in this article, generate more relevant recommendations.
Moreover, we would like to repeat our offline experiments by using other datasets,
such as that collected by MyPersonality, a Facebook app for taking psychometric tests
by users. The users who took the tests using this app came from different age groups,
backgrounds and cultures. That makes the dataset unique and valuable. This dataset,
currently, has more than 6,000,000 test results together with more than 4,000,000
Facebook profiles of the users who took the tests. (My Personality Project, 2014). For
instance, there are more than 900,000 results for the big-five personality test. By
repeating our experiments on this larger dataset we can obtain a stronger confirmation
of our working hypotheses.
Finally, we would like to investigate the possibility of mining the social network
accounts of the users of STS in order to identify their personality. In fact, it has been
shown that the personality of the users can be learnt from their interactions in social
networks (Bachrach et al., 2012), and afterward, be used in collaborative filtering
based RSs (Fernández-Tobías et al., 2014). This is particularly interesting for the
users that skip the personality questionnaire during the registration phase.
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... However, their results were so poor, and they advised utilizing the rich content of online reviews to represent items and understand users. Demographic information may enhance the quality of recommendations when incorporated as a part of a hybrid recommender system [21]. ...
... The second category of TRSs is location-based recommender systems, including points of interest (POIs), travel destination, and context-aware recommender systems (CARSs). Most of POIs recommender systems tend to recommend nearby locations to the most recent check-in or the active user's current location [21], [24]- [26]. This can be helpful in the late stages of the travel decision. ...
... Braunhofer et al. [21] built a hybrid TRS combining demographic and human personality traits with collaborative filtering in the process of rating prediction. They relied on explicit personality acquisition since it has higher accuracy and takes less effort from users to provide their personality information. ...
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Travel recommender systems (TRSs) aim to reduce travel-related search overload. A significant part of a TRS is representing attractions in a way that reflect the explicit and implicit features of attractions. However, traditional attraction representation methods may not provide a complete image of attractions. Building on the notions of user travel styles (UTSs) and the wisdom of crowds, we propose a method derived from topic-model-based models to represent travel attractions, called the Normalized Attraction Travel Personality (NATP) representation. This approach attempts to leverage the semantics of attraction reviews to model user travel personalities (UTPs), which collectively can construct the attraction travel personality (ATP) representation. Furthermore, we regularize and normalize the ATP representation to obtain our proposed representation. This NATP-based attraction representation could capture implicit characteristics of attractions revealed by the wisdom of crowds. Our experiments show that our representation method gained better results when evaluated against comparative approaches in terms of rating prediction and recommendation ranking quality, indicating the effectiveness of the proposed attraction representation. Lastly, we qualitatively investigate how our attraction representation surpasses the state-of-the-art representation methods. INDEX TERMS Content-based filtering, attraction representation, knowledge discovery, travel styles, travel recommender systems.
... -User preferences change over time. In addition, context influences user selection [14,15]. ...
... Step 2. The values set obtained in (15) is ordered in descending order -a permutation is formed (9). The resulting permutation is the solution of the problem. ...
The paper presents the theoretical and algorithmic aspects for making a personalized recommender system (mobile service) designed for public route transport users. The main focus is on identifying and formalizing the concept of "user preferences", which is the basis of modern personalized recommender systems. Informal (verbal) and formal (mathematical) formulations of the corresponding problems of determining "user preferences" in a specific spatial-temporal context are presented: the preferred stops definition and the preferred "transport correspondence" definition. The first task can be represented as a well-known classification problem. Thus, it can be formulated and solved using well-known pattern recognition and machine learning methods. The second is reduced to the construction of dynamic graphs series. The experiments were conducted on data from the mobile application "Pribyvalka-63". The application is the service part, currently used to inform Samara residents about the public transport movement.
... Transfer Learning. Firstly, in transfer learning, we mainly study the relationship between different samples [11][12][13], so we call these two different data sets domains, and the work of migrating from one domain to another domain is called a task, which is the most basic concept to explore this problem. e source domain and target domain are the terms used to describe these two distinct realms. ...
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With the development of information technology and the popularity of the Internet, the data on the network is growing exponentially. Information overload has become a significant issue for consumers seeking information. A recommendation system was created to detect users’ interests from huge amounts of data and to suit users’ specific information needs. Traditional collaborative filtering recommendation mostly uses scoring data for a recommendation, which has the problem of sparse data, which limits the performance of the recommendation system. On this basis, this paper studies the personalized recommendation algorithm of scenic spots with deep migration. Through the analysis of collaborative filtering recommendation methods, it is found that the traditional collaborative filtering methods only use scoring data for a recommendation, which has the problem of sparse data. Based on the vectorization of user interest, the similarity of user preference is calculated, and the matrix decomposition is carried out in cooperation with user implicit feedback, to integrate the knowledge transfer information into the matrix decomposition model, and make up for the lack of considering the attribute information of scenic spots in the matrix decomposition algorithm, and alleviate the problem of data sparsity. The findings of comparative trials suggest that the personalized scenic location recommendation approach proposed in this study, which is based on the depth migration algorithm, is effective. Compared with the benchmark recommendation method, the recommendation accuracy and recall rate has been improved to a certain extent.
Large volumes of end-user-generated textual data are assembled every day which leads to the evolution of social media in the form of reviews/feedback, and brief description messages. As a consequence, end-user often see it difficult to understand more concerning the subject being discussed or appropriate knowledge from such material. The enormous amount of text-oriented data that is accessible via the online platform is analyzed using machine learning and natural language processing algorithms, including topic modeling techniques that have been more prevalent in current years. The novel approach is proposed to represent travel categories called the Normalized Category Travel Personality (NCTP). The main purpose of the technique is to construct the semantics of category feedback to model travelers’ interests to create the category travel personality (CTP) representation. Likewise, we normalize the CTP to obtain our proposed model. The NCTP category model will apprehend the explicit and implicit aspects of categories shown by the social-circle groups to find sentiment scores. The TripAdvisor dataset was considered to evaluate the performance of the NCTP model based on the topic-model quality, implicit and explicit characteristics, and some legacy statistical evaluation metrics, like Recall, Mean Average Precision, and Mean Reciprocal Rank.
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Recommender systems (RSs) are personalized information search and discovery applications helping users to identify and choose useful items and information. In this paper, we focus on the tourism application scenario and its specific requirements. We discuss a novel RS approach that copes with the specific application constraints of the domain and produces recommendations that better match the true needs of tourists. We illustrate the proposed next POI recommendation approach in a case study and we compare it with a state‐of‐the‐art nearest neighbor‐based next item RS. With the analysis of this case study, we aim at illustrating the specific features of the compared approaches also with the goal to raise the discussion on RSs validation methods, with a particular attention to tourism applications. We finally discuss some significant limitations of current evaluation approaches that must be addressed in future studies.
Identifying personality traits in children is a topic of interest due to its importance in adapting strategies for the teaching–learning process and detecting psychopathological features. The most straightforward procedure to identify children’s personality type is by applying a validated and suitable questionnaire according to their age. However, an interesting approach is automatically identifying the children’s personalities using their speech generated during their interaction with computer systems, software, or robots. This approach would allow obtaining the personality identification transparently to the children without answering a written test. This article presents a method for the automatic personality assessment of children between 8 and 12 years old. The assessment is based on their voices’ acoustic analysis while participating in playful activity with another child and a robot. We created a database with 98 children involved in several activities while their voices were recorded. The database was labeled with five paralinguistic aspects. Using these labels, we trained a set of classification models that helped us recognize primary and secondary personality traits according to the Children’s Personality Questionnaire. We obtained good results for two secondary personality traits, extroversion (0.89 F-Score) and excitability (0.79 F-Score). The best F-score obtained for anxiety was 0.70. These results indicate that it is feasible to estimate personality from analyzing children’s voices during interaction with computer systems.
Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people’s mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration into their regular routines reducing the risk of infection. In this context, this paper presents a solution for crowd management. The aim is to monitor and manage crowd levels in interior places or point-of-interests (POI), particularly shopping centers or stores. The solution is based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user. In this sense, it recommends places near the user location with the least estimated crowd. The recommendation algorithm uses a top-K approach and behavioral game theory to predict the user’s choice and estimate the crowd level for the requested POI. To evaluate the efficiency of this technological intervention in terms of the potential number of contacts of possible COVID-19 infections and the recommendation quality, we have developed an agent-based model (ABM). The adoption level of new technologies can be related to the end-user experience and trust in such technologies. As the end-user follows a recommendation that leads to uncrowded places, both the end-user experience and trust increased. We study and model this process using the OCEAN model of personality. The results from the studied scenarios showed that the proposed solution is widely adopted by the agents, as the trust factor increased from 0.5 (initial set value) to 0.76. In terms of crowd level, these are effectively managed and reduced on average by 40%. The mobility contacts were reduced by 40%, decreasing the risk of COVID-19 infection. An APP has been designed to support the described crowd management and contact tracing functionality. This APP is available on GitHub.
The rapid development of IoT sensors and data provided by Social Networks has necessitated the fast development of recommender systems as they can be used as a tool to filter items that are more likely to be preferred by users. A major goal of recommender systems is to provide users with personalized recommendations after analyzing their preferences. IoT smart devices and Social Networks have opened windows of opportunities for user preferences to be dynamically recognized. Although analyzing user preferences helps to provide more personalized recommendations, considering location and orientation information as user contextual information results in more relevant recommendations being provided. Location information has been used by Social Networks, especially Location-Based Social Networks, in order to provide recommendations based on current user location. However, the importance of the user orientation context has been overlooked by almost all of the research done in this area. Developing a location-based orientation-aware recommender system can perfectly bridge this gap. For this study, a location-based orientation-aware recommender system is proposed as an innovative type of recommender system. The proposed recommender system is able to not only apply contemporary user contextual information to the recommender algorithm, but also makes progress towards preparing more personalized recommendations by taking user orientation context into account. For this study, user preferences are dynamically measured by IoT smart devices such as smartphones, Google Home, and smartwatches. Information provided by virtual communities extracted from Social Networks helps the recommender system in situations in which user preferences are not extracted from their IoT devices. In addition to user preferences, their smartphone pointing direction has also been applied as their orientation context for the recommender algorithm in outdoor environments. To evaluate the impact of the user pointing direction in our proposed methodology, an event recommender system based on the real data was implemented and examined in the city of Tehran in Iran. Because of the challenging nature of social events, a simulated experiment is also presented for the City of Calgary. Also, the system results are compared with the results of Collaborative Filtering and Content-based recommender algorithms to demonstrate the power of the recommendation engine. The evaluation indexes prove that our proposed recommender system outperforms its counterparts by providing more accurate and personalized recommendations.
Conference Paper
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In Collaborative Filtering Recommender Systems user’s preferences are expressed in terms of rated items and each rating allows to improve system prediction accuracy. However, not all of the ratings bring the same amount of information about the user’s tastes. Active Learning aims at identifying rating data that better reflects users’ preferences. Active learning Strategies are used to selectively choose the items to present to the user in order to acquire her ratings and ultimately improve the recommendation accuracy. In this survey article, we review recent active learning techniques for collaborative filtering along two dimensions: (a) whether the system requested ratings are personalised or not, and, (b) whether active learning is guided by one criterion (heuristic) or multiple criteria.
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Weather plays an important role in tourists’ decision-making and, for instance, some places or activities must not be even suggested under dangerous weather conditions. In this paper we present a context-aware recommender system, named STS, that computes recommendations suited for the weather conditions at the recommended places of interest (POI) by exploiting a novel model-based context-aware recommendation technique. In a live user study we have compared the performance of the system with a variant that does not exploit weather data when generating recommendations. The results of our experiment have shown that the proposed approach obtains a higher perceived recommendation quality and choice satisfaction.
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This study directly tests the effect of the "Big Five" personality traits on smartphone ownership and use. Although researchers have tested the impact of personality the use of on communication technology, this is the first study that specifically examines smartphone use. Logistic regression and hierarchical linear regression were used to analyze results from a sample of 312 participants. We found that extraverted individuals were more likely to own a smartphone. Also, extraverts reported a greater importance on the texting function of smartphones. More agreeable individuals place greater importance on using the smartphone to make calls and less importance on texting.
Conference Paper
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Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user's personality -using the Five Factor Model (FFM) -in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, context-aware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.
Conference Paper
Finding effective solutions for cold-starting Context-Aware Recommender Systems (CARSs) is important because usually low quality recommendations are produced for users, items or contextual situations that are new to the system. In this paper, we tackle this problem with a switching hybrid solution that exploits a custom selection of two CARS algorithms, each one suited for a particular cold-start situation, and switches between these algorithms depending on the detected recommendation situation (new user, new item or new context). We evaluate the proposed algorithms in an off-line experiment by using various contextually-tagged rating datasets. We illustrate some significant performance differences between the considered algorithms and show that they can be effectively combined into the proposed switching hybrid to cope with different types of cold-start problems.
Conference Paper
In this paper we investigate the incorporation of information about the users’ personality into a number of collaborative filtering methods, aiming to address situations of user preference scarcity. Through empirical experiments on a multi-domain dataset obtained from Facebook, we show that the proposed personality-aware collaborative filtering methods effectively-and consistently in the studied domains-increase recommendation performance, in terms of both precision and recall. We also present an analysis of relationships existing between user preferences and personality for the different domains, considering the users’ gender and age.
Conference Paper
Most of the existing recommender systems for tourism apply knowledge-based and content-based approaches, which need sufficient historical rating information or extra knowledge and suffer from the cold start problem. In this paper, a demographic recommender system is utilized for the recommendation of attractions. This system categorizes the tourists using their demographic information and then makes recommendations based on demographic classes. Its advantage is that the history of ratings and extra knowledge are not needed, so a new tourist can obtain recommendation. Focusing on the attractions on Trip Advisor, we use different machine learning methods to produce prediction of ratings, so as to determine whether these approaches and demographic information of tourists are suitable for providing recommendations. Our preliminary results show that the methods and demographic information can be used to predict tourists' ratings on attractions. But using demographic information alone can only achieve limited accuracy. More information such as textual reviews is required to improve the accuracy of the recommendation.