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Balancing Preferences, Popularity and Location in Context-Aware Restaurant Deal Recommendation: A Bristol, Cardiff and Brighton Case Study
Abstract
We propose a personalisation solution to recommend tailored restaurant deals for residents or visitors in a city. Unlike previous work on recommendations in the restaurant sector where actual venues are recommended, we focus on suggesting specific products in the form of deals offered by such restaurants. This is done by jointly filtering relevant information for the end-user based on their food-drink preferences, the popularity of the restaurant, its proximity to the user's location and temporal constraints on the availability of deals. A real case study has been conducted upon datasets provided by Wriggle, a platform for discovering local deals in various cities across England.
Many tourism recommender systems have been studied to offer users the items meeting their interests. However, it is a non-trivial task to reflect the multi-criteria ratings and the cultural differences, which significantly influence users’ reviews of tourism facilities, into recommendation services. This paper proposes two “single tensor” models, consisting of users (or countries), items, multi-criteria ratings, and cultural groups, in order to consider simultaneously an inherent structure and interrelations of these factors into recommendation processes. With one Tripadvisor dataset, including 13K users from 120 countries, experiments demonstrated that, in terms of MAE, the two proposed models for user and country give an improvement of 21.31% and 7.11% than other collaborative filtering and multi-criteria recommendation techniques. Besides, there were the positive influences of multiple-criteria ratings and cultural group factors on recommendation performances. The comparative analysis of several variants of the proposed models showed that considering Western and Eastern cultures is appropriate for improving predictive performances and their stability.
Recommender systems have been one of the main
methods to overcome the information overload problem in many
domains including tourism. A solo traveller may easily create
an itinerary according to their taste. However, when it comes to
a group of people, it is often difficult to find the most suitable
places to please everyone’s preferences, not only because of
lack of prior information about a place but also owing to the
difficulty to take influence among group members into account.
In our work, we introduce a model for Group Decision Making
that uses YouTube API to gather rich video contents associated
with a finite set of alternatives, e.g. travel destinations. Instead
of eliciting subjective opinions on alternatives directly, prefer-
ences are built upon a Collaborative Filtering approach, based
on each participant’s watch history and interaction with items.
Randomly chosen videos from different group members are
also recommended to each target user in order to infer trust
information within the group. We then use trust information to
obtain an aggregated group preference for determining places
to visit. An application example based on YouTube API shows
that a higher degree of interaction cons
In this demo paper, we present TourRec, amobile Recommender System (RS) for tourist trips, sequences of points of interest (POIs) along enjoyable routes. The core of TourRec is a modular, multi-tier architecture facilitating the development and evaluation of new recommendation algorithms, clients and data sources. We show how the TourRec Android application can be used to recommend tourist trips to individuals and groups. Furthermore, we explain how TourRec supports the evaluation of different recommendation algorithms and group recommendation strategies. A video demonstrating how TourRec works is available under https://youtu.be/o_yx8UGvvvo.
SigTur/E-Destination is a Web-based system that provides personalized recommendations of touristic activities in the region of Tarragona. The activities are properly classified and labeled according to a specific ontology, which guides the reasoning process. The recommender takes into account many different kinds of data: demographic information, travel motivations, the actions of the user on the system, the ratings provided by the user, the opinions of users with similar demographic characteristics or similar tastes, etc. The system has been fully designed and implemented in the Science and Technology Park of Tourism and Leisure. The paper presents a numerical evaluation of the correlation between the recommendations and the user's motivations, and a qualitative evaluation performed by end users.
The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including
e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management.
While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches
focus on recommending the most relevant items to users without taking into account any additional contextual information,
such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue
that relevant contextual information does matter in recommender systems and that it is important to take this information
into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender
systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling
– for incorporating contextual information into the recommendation process, discuss the possibilities of combining several
contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach.
Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions
for future research.
Cross-Domain Recommendation in the Hotel Sector
- M Al-Ghossein
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The Limits of Popularity-Based Recommendations, and the Role of Social Ties
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Large Group Decision Making: creating Decision Support Systems at Scale. Springerbriefs in Computer Science
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