REPORT ON CBRECSYS 2015

Report on RecSys 2015 Workshop on NewTrends in Content-Based Recommender Systems
Toine Bogers1
Marijn Koolen2
toine@hum.aau.dk marijn.koolen@uva.nl
1
Aalborg University Copenhagen, Denmark2
University of Amsterdam, Netherlands

Abstract
This article reports on the CBRecSys 2015 workshop, the second edition of the workshop on new trends in content-based recommender systems, co-located with RecSys 2015
in Vienna, Austria. Content-based recommendation has been applied successfully in manydifferent domains, but it has not seen the same level of attention as collaborative filtering
techniques have. Nevertheless, there are many recommendation domains and applications
where content and metadata play a key role, either in addition to or instead of ratings andimplicit usage data. The CBRecSys workshop series provides a dedicated venue for work
dedicated to all aspects of content-based recommender systems.

1

Motivation and Goals

While content-based recommendation has been applied successfully in many different domains[10], it has not seen the same level of attention as collaborative filtering techniques have. In recentyears, competitions like the Netflix Prize1 , CAMRA2 , and the Yahoo! Music KDD Cup 2011[6] have spurred on advances in collaborative filtering and how to utilize ratings and usage datafrom a community of users. However, there are many recommendation domains and applications
where item content and metadata play a key role, either in addition to or instead of ratings andimplicit usage data. For some domains, such as movies, the relationship between content andusage data has seen thorough investigation already (e.g. [12]), but for many other domains, suchas books, news, scientific articles, and Web pages we still do not know if and how these data
sources should be combined to provide the best recommendation performance. On top of that,in certain contexts of search, content-based recommendation plays a role as well. Personalised IR
is strongly related to recommendation, but recent research in book search also identified search
scenarios that combine aspects of retrieval and recommendation [7].
12

http://www.netflixprize.com/
http://www.dai-labor.de/camra2010/

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The CBRecSys workshop series aims to address this by providing a venue for papers dedicatedto all aspects of and new trends in content-based recommender systems. This includes both recommendation in domains where textual content is abundant (e.g., books, news, scientific articles,
jobs, educational resources, and Web pages) as well as dedicated comparisons and combinationsof content-based techniques with collaborative filtering approaches. After a successful first edition
organized at RecSys 2014 in Silicon Valley [3, 4], the second edition of the workshop, CBRecSys
2015, was co-located with RecSys 2015 in Vienna, Austria.

2Workshop Focus & Format

Authors were encouraged to submit papers on a variety of topics related to content-based recommendation, including:• Developing novel recommendation approaches• Exploiting user-generated content for recommendation• Processing text reviews• Mining contextual data from content• Addressing limitations of recommender systems
In particular, papers submitted to the the workshop focused on the following topics. Several
papers looked at how semantic information in the form of Linked Open Data resources can be integrated in recommendation algorithms. Other papers investigated solutions to common problems
for recommender systems, such as ensuring diverse recommendations and dealing with continuous cold-start scenarios. Finally, a handful of submissions proposed novel ways of incorporating
metadata features into collaborative filtering algorithms for domains such as fashion and books.
A total of 12 papers were submitted, of which 8 were accepted for an oral presentation (for anacceptance rate of 66%). All papers were reviewed by at least 3 reviewers. In terms of attendance,CBRecSys 2015 proved to be a popular workshop with around 40 attendees.

2.1

KeynoteThe workshop started with a keynote presentation on how to capture user interests for contentbased recommender systems by Frank Hopfgartner (University of Glasgow, UK). This keynote
gave a great introduction to the concepts and ideas behind content-based recommendation andthe advantages of using semantic metadata. The presentation highlighted the challenges that comewith an automated analysis of content, especially in the multimedia domain and identified different
methods of capturing user interest. The keynote finished with a description of and invitation toparticipate in the NewsREEL challenge3 , an evaluation campaign that allows researchers to benchmark news article recommendation algorithms in both an offline and online setting. The challenge
offers a great platform for investigating content-based and hybrid recommendation techniques.
3

Available at http://www.clef-newsreel.org/, last visited October 7, 2015.

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2.2

Accepted Papers

This section briefly summarizes the eight accepted papers presented at the workshop. In theirwork, Tomeo et al. [14] addressed the issue of diversity in recommendation and proposed a model
to infer intents from user profiles. User intents are modelled by multiple attributes of items using
regression trees. The approach is evaluated on data sets from the domains of books and movies,
with rule-based diversification leading to good diversity in recommended items across users, andin combination with traditional intent-aware algorithms leading also to good diversity within asingle user’s recommendations.
Musto et al. [11] presented a graph-based approach to recommdation by extracting item features from the Linked Open Data cloud. They compared a range of feature selection techniquessuch as PageRank, PCA, SVM and Information Gain, and found that specific selection techniques can be used to optimize for accuracy or diversity. The overall method outperforms several
state-of-the-art baselines.
Kula [8] introduced LightFM, a hybrid model to address the cold-start problem for domains
where item metadata is diverse and sparse. LightFM is a hybrid matrix factorization model thatuses embeddings of metadata and natural language descriptions as latent features to represent
both items and users, with which it can easily generalize to new items and users. The model
naturally blends metadata and natural language descriptions into a single model.
De Graaff et al. [5] developed and evaluated a knowledge-based recommender system that isdomain-independent, language-independent, and independent of the used social medium. The
user interests are derived from the users social media profile, enriched with information fromDBpedia. An user study showed that this very generic model can improve a domain specificbaseline, indicating the complementarity of generic interests.
Bernardi et al. [2] argued that in e-commerce domains such as travel, recommender systems
have to deal with continuous cold-start problems for known users, due to users returning infrequently, in different roles, with interests that change as users go through different phases of theirlife. The paper explores this problem and points out some important consequences, supported byexamples from Booking.com, a major travel recommendation website.
Poussevin et al. [13] proposed to augment recommender systems by generating personalized
reviews associated to items, as well as predict ratings and providing personalised sentiment classification. To personalize the reviews for a user, similar users are found based on the textual
similarity between their reviews and a user’s own reviews. After that, extractive summarization
is used to generate the personal review.
Benkoussas and Bellot [1] presented a hybrid algorithm for solving the problem of book searchand recommendation. They combined different retrieval models for the content-based recommendation part with a graph-based algorithm.
Labille et al. [9] proposed a recommender system for CiteSeerX, a digital library for computerscience research, that takes both the content and the impact of articles and papers into account.
Their hybrid recommender integrates scientific impact in the form of the authors’ h-indexes asa part of the ranking criteria and was shown to produce more relevant paper than the baseline
content-based recommender system already in use at CiteSeerX.

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2.3

Breakout Session on Current and Future Challenges

The workshop was concluded by a breakout session on current and future challenges in contentbased recommender systems. A research topic that came up throughout the session is the orderin which to recommend items, the order in which they are consumed, and the order in which new
items become available to recommend. This is a well-known issue in real-time scenarios in social
media and news, but there are many other domains in which this plays an important role, such as
scientific literature, health, job search (e.g. modeling career paths) and especially in e-learning.
Issues such as novelty and tracking the development of a user’s knowledge and interests and thedevelopment of topics and genres as new items become available is poorly understood.
Another important property of a good recommender system is the ability to generate explanations for why the items are being recommended and what kind of data is used to recommend
them. Participants agreed that the relative importance of this depends on the application domain,with participants from different backgrounds arguing for the relative importance in their domain.
A recurring topic in recommender systems and IR research in general is diversity. Being able
to produce diverse recommendations was seen as another important property of a good contentbased recommender, although the necessity depends on the domain: for scientific articles or music,
diversity is more important than for recommending job listings. Participants also expressed adesire for more research on whether and how users should be able to control the diversity andnovelty of different recommendation algorithms.
The participants also discussed the desire for and problems with a RecSys challenge. Everyone
agreed that a joint challenge in which many teams tackle the same problem and use the samedata set and evaluation setup is a great way to advance the state-of-the-art in (content-based)recommendation. However, such joint challenges reveal a tension between the interests of academia
and those of industry. Researchers in industry have their own, very specific problems to deal withand are understandably hesitant to invest time and resources elsewhere. Questions raised during
the discussion where: What are topics of interest for both academics and industry? What are
the criteria that such a challenge should satisfy for industry to consider spending their resourceson it? What are domains where there are good resources for data and shared interest in research
problems? It was suggested that Kaggle4 is a good platform for getting many participants, butwith the severe caveats that it does not allow challenge organizers to use their own evaluation
measures and that there is little overlap between the communities of Kaggle and RecSys, withKaggle participants feeling little urge to go to workshops and conferences to present their work.
When asked about the possibility for contributing data to such a challenge, it was evident thatcompanies are less likely to do so after the privacy issues of the AOL search logs and the firstNetflix prize. Nevertheless, living lab initiatives for recommendation—such as the NewsREEL
news recommendation challenge organized in conjunction with Plistawere seen as steps in theright direction.
4
Kaggle is a platform for data mining and predictive modeling competitions; see http://www.kaggle.com/ formore information.

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3Future Workshop

The continued popularity of the CBRecSys workshops in terms of both submissions and participation clearly shows a consistent interest in research on content-based recommendation. We are
therefore planning a third edition of the CBRecSys workshop at RecSys 2016 in Boston.

4Acknowledgments

We would like to thank ACM and RecSys for hosting this workshop, the RecSys workshop chairs
Alan Said and Tsvi Kuflik. We would also like to thank the program committee: Robin Burke,Iván Cantador, Federica Cena, Paolo Cremonesi, Ernesto William De Luca, Tommaso Di Noia,Peter Dolog, Soude Fazeli, Cristina Gena, Juan Fernando Huete, Jaap Kamps, Birger Larsen,
Babak Loni, Pasquale Lops, Cataldo Musto, Casper Petersen, Shaghayegh Sahebi, Alan Said,
Giovanni Semeraro, Nafiseh Shahib, Marko Tkalĉiĉ, and Bei Yu. Final thanks are due to thepaper authors, the keynote speaker Frank Hopfgartner, and the participants for a great and livelyworkshop.
The workshop material (list of accepted papers, invited talk, and the workshop schedule) can
be found on the CBRecSys 2015 workshop website at http://humanities.uva.nl/~mkoolen1/
CBRecSys15/. The proceedings were published as a CEUR Workshop Proceedings volume, available at http://ceur-ws.org/Vol-1448/.

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