Accuracy in Rating and Recommending Item
Lloyd Rutledge1?, Natalia Stash2, Yiwen Wang2, and Lora Aroyo3
1Telematica Instituut, Enschede, The Netherlands
2Technische Universiteit Eindhoven, Eindhoven, The Netherlands
3Vrije Universiteit, Amsterdam, The Netherlands
Abstract. This paper discusses accuracy in processing ratings of and
recommendations for item features. Such processing facilitates feature-
based user navigation in recommender system interfaces. Item features,
often in the form of tags, categories or meta-data, are becoming impor-
tant hypertext components of recommender interfaces. Recommending
features would help unfamiliar users navigate in such environments. This
work explores techniques for improving feature recommendation accu-
racy. Conversely, it also examines possibilities for processing user ratings
of features to improve recommendation of both features and items.
This work’s illustrative implementation is a web portal for a museum
collection that lets users browse, rate and receive recommendations for
both artworks and interrelated topics about them. Accuracy measure-
ments compare proposed techniques for processing feature ratings and
recommending features. Resulting techniques recommend features with
relative accuracy. Analysis indicates that processing ratings of either fea-
tures or items does not improve accuracy of recommending the other.
Recommender systems have acquired an important role in guiding users to items
that interest them. Traditionally, recommendation systems work exclusively with
tangible objects (such as films , books or purchasable products ) as what
they let users rate and what they consequently recommend. More recently, how-
ever, abstract concepts related to such items play an increasingly important role
in extended hypertext environments around recommender systems. For exam-
ple, Amazon.com’s recommender system1lets users select categories to fine-tune
recommendation lists. In addition, Amazon.com lets users assign tags to items,
which extends not only search for and navigation between items but also rec-
ommendation of them. As tags, categories and other concepts become more
important to users in interaction with recommender systems, users will benefit
from help with finding appropriate ones. The context of recommender systems
offers an obvious tool for this: the rating and recommendation of such concepts.
?Lloyd Rutledge is also affiliated with CWI and the Open Universiteit Nederland
Fig.1. CHIP Artwork Recommender display
Such rating and recommending of abstract concepts occurs in the CHIP
project Artwork Recommender2. Figure 1 shows an example display. The
system’s users can rate and recommend items in the form of artworks from the
collection of the Rijksmuseum Amsterdam. Users can also rate and recommend
features in the form of abstract topics (such as artist, material and technique)
related to these artworks, which fall in a hyperlinked network joining artworks
with related topics and topics with each other. Recommending artworks and
topics brings users to interface displays from which they can rate related artworks
and topics, improving their profiles. In this process, users not only find artworks
they like, they learn about personally interesting art history topics that affect
their taste. Studies show that users benefit from feature recommendation in such
an integrated environment .
This paper explores how to maximize both the accuracy of feature recom-
mendation and the exploitation of feature ratings. It starts by discussing related
work and describing the evaluation methods it applies. The first core section dis-
cusses the differences between how users rate features and how they rate items.
The paper then shows the impact on collaborative filtering accuracy that feature
rating and recommending bring. The final core section proposes an adaptation of
established content-based techniques to recommend features. This paper wraps
up with conclusions from the study.
2 Related Work
This section discusses work related to navigating and rating features in recom-
mender systems. This work tends to fall in the separate subfields of feature-based
navigation in recommender systems, rating of tags and browsers for extensively
annotated items. These fields combine in the implementation this work applies
in exploring recommender accuracy for these topics.
Amazon.com uses both categories and tags in its recommender service by let-
ting users specify that each can refine recommendation lists. Amazon, however,
lets users rate neither categories nor tags, only items. In addition, they do not
use categories and tags in recommendation generation processing. MovieLens
recently added tags to its recommender service, giving users both the ability to
assign tags to movies and to rate tags assigned by others . This rating of tags
relates closely to this work’s rating of features. In MovieLens, however, a user’s
rating of a tag indicates the user’s confidence in its informative accuracy rather
than how appealing the user finds that topic.
While Amazon.com and MovieLens only let users rate items, Revyu.com
lets users rate “anything” by assigning ratings (and descriptive reviews) to
community-defined tags . These ratings represent level of user interest.
Revyu.com does not distinguish between items and their features because tags
can represent either in the same manner. However, Revyu.com does not process
these ratings for recommendations.
Amazon.com and MovieLens offer tags as part of recommendation, provid-
ing community-defined features. Amazon.com also provides centrally maintained
item features in the form of categories. Facetted browsers offer the current state-
of-the-art for accessing items by exploiting their centrally maintained features,
where the features are more complex in nature. Typically, with facetted browsers,
items can have many features and each feature is a property assignment using
one of multiple property types. The E-Culture browser3offers such facetted
access for museum artworks, processing data for over 7000 artworks from multi-
ple institutions that cooperatively apply common vocabularies in making typed
properties of these artworks . The annotations from the CHIP Artwork Rec-
ommender use the same vocabularies and property types, which has enabled
incorporation of the Rijksmuseum artworks and annotations into the E-Culture
Studies with the CHIP Artwork Recommender show that coordinated rating
and recommending of features with items improves how novices learn art topics
of interest . Other studies with this system show that explaining item rec-
ommendation in terms of common features is important for user assessment of
recommender system competence and other aspects of trust . This work now
performs similar accuracy analyses for feature recommendation and for process-
ing feature ratings for recommendation in general.
This section presents the methods that evaluate the techniques this work pro-
poses. It first discusses the user tasks to which the evaluating measurements ap-
ply. It then describes the application of the leave-n out approach that provides
accuracy measurements here. This section wraps up by presenting the specific
metrics for measuring the satisfaction of these user tasks.
The CHIP recommender interface display in Figure 1 illustrates several user
tasks. This work’s evaluation focuses on two of these tasks. Both involve pro-
viding recommendations as a list of all things the user is likely to like. One task
is showing all recommendations of items, to which the interface provides access
from the link “See all recommended artworks” at the bottom right of Figure 1.
The area above this links shows the top five of these recommendations. The sec-
ond task is show all recommended features, which the link “See all recommended
topics” links to at the bottom left of Figure 1.
This work’s evaluations of the techniques it proposes apply the leave-n out
approach. This starts by withholding 10% of the sample ratings as a truth set.
The algorithms to evaluate then process the remaining ratings to calculate pre-
dictions for the ratings in this truth set. Comparing the predictions with their
corresponding true values forms the basis for the various metrics these evalua-
The metrics that this work calculates in its evaluations are NMAE, precision
and recall. They are common in recommender system and information retrieval
research. As both main user tasks involve retrieval of all appropriate matches,
these classic metrics of precision and recall apply well.
The NMAE (normalized mean absolute error) measures predictive accuracy
by showing by what percentage the system’s predictions for the truth set ratings
differ from their real values. The remaining metrics provide classification accu-
racy, which measures how well the system generates list of recommendations.
Precision shows how many of the recommendations the user truly likes. Recall
indicates how many desired items and features appear among the recommenda-
tions. Precision and recall depend on a recommendation threshold, which is a
value above which predicted ratings form recommendations for their correspond-
ing concepts. That is, the system recommends an item or feature if the predicted
interested for it exceeds this threshold.
4 User Ratings for Items and Features
This section discusses patterns that emerge in comparing how users rate items
with how they rate features. This analysis uses two sets of ratings for artworks
and related art topics entered by users of the CHIP Artwork Recommender. One
set of ratings comes from the online demo, with no restriction on use. The other
is from a directed user study. By having ratings for both artworks and topics,
this set represents ratings for items and features respectively.
Figure 2 shows the distribution of these ratings across the users. The sample
sets for this current work includes only users who gave at least 10 ratings, of
which at least one is for a feature, in order to ensure there is substantial data
from each user from which to calculate recommendations. The bar charts on the
right half of Figure 2 show overall a large amount of five-star (value is 1) and
four-star (value is 0.5) ratings.
One rating set comes from the main online demo for the CHIP Artwork
Recommender. These users have “free use” of the online demo in the sense that
they are unsupervised and can have as many sessions as they want whenever
they want with no particular tasks to fulfill and no restrictions in how to use the
demo. This usage represents the general target use of a recommender system.
# features rated
2842 item ratings
1096 feat. ratings
# items rated
830 item ratings
1436 feat. ratings
# features rated
# items rated
Free-use Online Demo
Directed User Study
Ave. = 32.3
Ave. = 25.2 Ave. = 43.5
Ave. = 12.5
Fig.2. User-rating distributions
0% 10% 20% 30%
20% 40% 60%80%
CF rec. categ. ratings
CF both rating sets
Item Recommendation Feature Recommendation
Free-use Online Demo
Directed User Study
Fig.3. Accuracy charts for proposed techniques
One pattern that Figure 2 shows is that users given free use of such a system
tend to enter many more, in this case almost three times as many, ratings for
items as they do for features. Another pattern is that feature ratings tend to be
more positive and extreme than item ratings. Users were almost twice as likely
to rate a feature with five stars (value is 1) than an item. They were also almost
twice as likely to rate an item as neutral (value is 0) than a feature. One possible
explanation is that users have more extreme opinions about features than items
because features are abstract generalizations whereas an item can have many
potentially contrasting characteristics that affect user interest in it.
Another possible explanation for the more frequently positive feature ratings
is that previous familiarity has a different impact on rating items than on rating
features. Perhaps users are more familiar with topics that influence their interest,
particularly if this influence is positive. Because users see images of items, they
can quickly make a rating for any item, even if they have not seen it before.
Features, on the other hand, appear as text labels instead of images, meaning
that users must be previously familiar with a topic to enter a rating for it.
While the previous rating set comes from free use of the online demo, another
sample set comes from a directed user study of this system. This study starts
by showing its users 45 topics, which this work considers features, and asking
the user to rate them. It then has the user interact with the main demo for a
minimum amount of time.
The directed user study brought different patterns in the charts in Figure 2
than for the free-use online demo. The two left-most charts, with distributions
of each type of rating across the users, are flatter than for the free-use demo.
One factor is that, in the directed study, ratings for each user came from a
single session with a time duration minimum. The plateau in the curve for the
distribution of feature ratings across users reflects the 45 features the study
asks users to rate. The values for the ratings also spread more evenly for the
directed study than for the free-use demo. This may be because the directed
study compels users to rate a particular variety of features and, to a lesser degree,
items. As with the free-use demo, users of the directed study tend to give more
positive ratings to features than to items, although the directed study’s feature
rating values tend to be more moderate.
5 Collaborative Filtering
Collaborative filtering (CF) is the determination of similarity patterns in ratings
from multiple users in order to recommend to a user what similar users rate
highly. This is typically the processing of item ratings to recommend items.
The software for CF that this work extends is the open source Duine toolkit
for recommender system frameworks4, which applies the Pearson correlation
coefficient . This section explores the impact on CF of both the rating and
recommendation of item features, showing that CF provides accurate feature
recommendation but does not improve accuracy when processing ratings from
both features and items together.
Figure 3 indicates the accuracy of the proposed techniques. It plots the corre-
sponding precision and recall values from 21 thresholds evenly spread in the full
range of rating values. The threshold for recommendation that the precision and
recall measurements here use is the top 20% of the range. The bar charts along
the right show predictive accuracy for these techniques. The top half comes from
the free-use set, while the bottom half comes from the directed user study. In
each case, the NMAE charts show measurements for recommending items and
features separately. Here, the bar “CBR” indicates the accuracy of content-based
recommendation, which the next section discusses. “CF both” measures the error
resulting from processing ratings of items and features together with collabora-
tive filtering. Finally, “CF same” processes only item ratings for recommending
items and feature ratings for recommending features.
The charts in Figure 3 show that, given this work’s rating sets, CF works as
well for features as for items. This indicates that systems can recommend features
with comparable accuracy as they do for items. One indication of this comparable
performance is in predictive accuracy, which the NMAE bar charts on the right
of Figure 3 show. Here, CF predictive accuracy from the larger feature rating set
has the same average error, if not slightly less, than CF recommendation from
the larger item rating set. This comparison uses the largest available set for each
recommendation category because CF relies on large amounts of ratings. As
Figure 2 shows, the larger set of feature ratings comes from the directed study,
which Figure 3’s bottom-most bar triple conveys. The larger set of item ratings
comes from the free-use demo, which the top-most bar trio conveys.
Another indication of CF’s accuracy in feature recommendation comes from
the precision-recall plot graphs in Figure 3. They convey that CF provides bet-
ter classification accuracy for recommending features than items. As with the
predictive accuracy comparison, this comparison is between CF for the larger
ratings sets: the predictive accuracy for the directed study’s feature ratings,
which the lower right plot graph in Figure 3 shows, with the predictive accuracy
for the feature ratings from the online demo in the upper left plot graph. The
curve for features is clearly higher, with the points for each threshold having
higher precision and recall than the corresponding points in the plot graph for
items. While it is tempting to conclude from this that features in general result
in such strongly more accurate CF classification than items, a primary factor in
the better classification in this comparison may be the strong overlap in ratings
between users for the directed study’s set of 45 topics. However, even if this
overlap screws the comparison, the conclusion would still be that CF provides
accurate classification at least when it compels users to rate overlapping sets.
While this work shows that accurate feature CF is possible, it fails to show
how feature ratings can benefit item CF, and vice versa. Figure 3’s NMAE bar
charts shows that combining ratings sets in CF provides less predictive accuracy
than CF processing of only ratings for the type of recommended concept. Here,
the “CF same” bars show accuracy for CF processing of ratings for the type
of recommendation, either for items or for features. The “CF both” bars show
accuracy for processing both item and feature ratings together and equivalently
for each type of recommendation. Although processing both ratings sets provides
more information than either alone, in all four cases there is either no discernable
change or substantial decrease in accuracy in CF for the combined rating sets.
Figure 3’s classification plot graphs show the equivalent degradation in accu-
racy for CF with combined rating sets. In three of the four graphs, the curve for
combined processing is clearly lower than the other CF curve. The exception is
feature CF with the free-use ratings, which show slight increase in precision but
larger decrease in recall. That CF accuracy for both prediction and classification
mostly decreases indicates that CF system should use only item ratings for item
recommendation and only feature ratings for feature recommendation.
One possibility for having CF improved accuracy by combine rating sets is to
treat items and features as domains in cross-domain mediation . This approach
shows that CF in one domain can improve with ratings from another by first
computing user similarity in each domain separately, combining them and then
applying the result for recommendation in the current domain. It remains an
open challenge for cross-domain mediation or other techniques to exploit user
ratings for either items or features to improve recommendation of the other.
6 Role-reversed Content-based Recommendation
Content-based recommendation (CBR) is the typical companion recommender
algorithm to CF. While CF uses similarities between users in terms of ratings
in order to recommend items similar users like, CBR uses similarities between
items in terms of their features in order to recommend items similar to other
items the current user likes. CF is typically more accurate than CBR when there
are enough ratings for enough items from enough users. However, in the cold-
start period leading up to this point, CBR typically performs better. Hybrid
recommender systems provide best overall recommendation by selecting which
of the two to apply in each recommendation situation . With the previous
section having established that CF can accurately recommend features, this sec-
tion adapts CBR to do so as well, providing in combination the components
needed for accurate hybrid recommendation of features. This adaptation is a
“role-reversed CBR” to recommend features instead of items.
Core issues in CBR include assigning appropriate features for the items to
recommend and determining appropriate processing for these features. This work
uses a CBR technique for processing features that are item properties encoded
with Semantic Web formats. The item and feature set are the museum artworks
and annotations from the CHIP Artwork Recommender. This paper adapts es-
tablished CBR algorithms in the following ways:
– treating semantically assigned properties as features (instead of keywords)
– assigning weights to features by adjusting tf-idf for frequency of properties
– processing cosine similarity on the resulting feature vectors
This technique represents the typical perspective of recommender systems, in
which items to recommend are tangible objects, with features that are abstract
concepts related to them. This section proposes role-reversed CBR for effective
feature recommendation as CBR that switches the roles items and features play
in its processing. That it, role-reversed processes features as “items” to recom-
mend and applies cosine similarity with tf-idf weights on vectors consisting of
the original items that each original feature annotates.
Figure 3 shows that this role-reversed CBR for features has similar precision
and recall as CF for features. The curves in both feature recommendation plot
graphs follow roughly the same pattern. For the free-use rating set, the curves
are very close. For the directed study set, however, CBR precision tends to be
less, albeit with roughly the same recall. A factor here may be that the second
set has more ratings overall and many more ratings per user, conditions which
typically improve CF in comparison to CBR. These measurements indicate that
role-reversed CBR provides effective cold-start feature recommendation and can
combine well with feature CF in hybrid systems for overall accurate feature
recommendation. As with CF, while CBR can accurately recommend features,
it remains an open challenge to have CBR exploit feature ratings to improve
item recommendation, and vice versa.
This paper shows how to improve feature recommendation and what role process-
ing ratings of either features or items has in recommending the other. Systems
can recommend features with accuracy that is comparable to item recommen-
dation. Techniques for doing so include CF and this work’s role-reversed CBR.
Users choose freely to rate features, although they rate items more frequently.
Users tend to rate features more positively than items. It remains a challenge in
both CF and CBR to have processing ratings for either items or features improve
the recommendation of the either.
This work was a collaboration with the Rijksmuseum Amsterdam within the
CHIP project5of the CATCH program6, funded by the Dutch Organization
for Scientific Research (NWO). The MultimediaN/E-Culture project7provides
the encoding of the Getty vocabularies that define some of this work’s items
features, along with mappings to them from original curator annotations. The
CATCH/STITCH project8provides an encoding for Iconclass, which defines
other item features this work uses. The implementation here uses the open source
Duine Toolkit9for recommendation processing. The Rijksmuseum Amsterdam Download full-text
gave permission for the use of its images.
1. L. Aroyo, R. Brussee, L. Rutledge, P. Gorgels, N. Stash, and Y. Wang. Personalized
museum experience: the Rijksmuseum use case. In Museums and the Web 2007,
San Francisco, USA, April 11-14 2007.
2. M. Balabanovic and Y. Shoham. Fab: content-based, collaborative recommenda-
tion. Commun. ACM, 40(3):66–72, 1997.
3. S. Berkovsky, T. Kuflik, and F. Ricci. Cross-domain mediation in collaborative
filtering. In The 11th International Conference on User Modeling (UM2007), pages
355–359, June 2007.
4. H. Cramer, B. Wielinga, S. Ramlal, V. Evers, L. Rutledge, and N. Stash. The
effects of transparency on perceived and actual competence of a content-based
recommender. In CHI 2008 Semantic Web User Interaction Workshop (SWUI
2008), Florence, Italy, April 5 2008.
5. N. Good, J. B. Schafer, J. A. Konstan, A. Borchers, B. M. Sarwar, J. L. Herlocker,
and J. Riedl. Combining collaborative filtering with personal agents for better
recommendations. In Proceedings of the Sixteenth National Conference on Artificial
Intelligence (AAAI-99), pages 439–446, Orlando, Florida, USA, July 18-22 1999.
6. T. Heath and E. Motta. Revyu.com: A reviewing and rating site for the web of
data. In The 6th International Semantic Web Conference (ISWC 2007), pages
895–902, Busan, Korea, November 2007.
7. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic frame-
work for performing collaborative filtering. In SIGIR ’99: Proceedings of the 22nd
annual international ACM SIGIR conference on Research and development in in-
formation retrieval, pages 230–237, New York, NY, USA, 1999. ACM Press.
8. G. Linden, B. Smith, and J. York. Amazon.com recommendations: item-to-item
collaborative filtering. Internet Computing, IEEE, 7(1):76–80, 2003.
9. G. Schreiber, A. Amin, M. van Assem, V. de Boer, L. Hardman, M. Hildebrand,
L. Hollink, Z. Huang, J. van Kersen, M. de Niet, B. Omelayenko, J. van Ossen-
bruggen, R. Siebes, J. Taekema, J. Wielemaker, and B. Wielinga. MultimediaN
E-Culture demonstrator. In Proceedings of the Fifth International Semantic Web
Conference (ISWC’06), pages 951–958, Athens, Georgia, USA, November 2006.
10. S. Sen, S. K. Lam, A. M. Rashid, D. Cosley, D. Frankowski, J. Osterhouse, F. M.
Harper, and J. Riedl. tagging, communities, vocabulary, evolution. In CSCW
’06: Proceedings of the 2006 20th anniversary conference on Computer supported
cooperative work, pages 181–190, New York, NY, USA, 2006. ACM.
11. Y. Wang, L. Aroyo, N. Stash, and L. Rutledge. Interactive User Modeling for
Personalized Access to Museum Collections: The Rijksmuseum Case Study. In
Proceedings of User Modeling 2007, pages 385–389, Corfu, Greece, June 2007.