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Motivation
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In recent years, increasing attention has been given to finding ways for combining, integrating and mediating heterogeneous sources of information for the purpose of providing better personalized services in many information seeking and e-commerce applications. Information heterogeneity can indeed be identified in any of the pillars of a recommender system: the modeling of user preferences, the description of resource contents, the modeling and exploitation of the context in which recommendations are made, and the characteristics of the suggested resource lists.

Almost all current recommender systems are designed for specific domains and applications, and thus usually try to make best use of a local user model, using a single kind of personal data, and without explicitly addressing the heterogeneity of the existing personal information that may be freely available (on social networks, homepages, etc.). Recognizing this limitation, among other issues: a) user models could be based on different types of explicit and implicit personal preferences, such as ratings, tags, textual reviews, records of views, queries, and purchases; b) recommended resources may belong to several domains and media, and may be described with multilingual metadata; c) context could be modeled and exploited in multi-dimensional feature spaces; d) and ranked recommendation lists could be diverse according to particular user preferences and resource attributes, oriented to groups of users, and driven by multiple user evaluation criteria.

The aim of HetRec workshop is to bring together students, faculty, researchers and professionals from both academia and industry who are interested in addressing any of the above forms of information heterogeneity and fusion in recommender systems.

The workshop goals are broad. We would like to raise awareness of the potential of using multiple sources of information, and look for sharing expertise and suitable models and techniques. Another dire need is for strong datasets, and one of our aims is to establish benchmarks and standard datasets on which the problems could be investigated.

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Topics of interest
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The goal of the workshop is to bring together researchers and practitioners interested in addressing the challenges posed by information heterogeneity in recommender systems, and studying information fusion in this context.

Topics of interest include, but are not limited to:

* Fusion of user profiles from different representations, e.g. ratings, text reviews, tags, and bookmarks
* Combination of short- and long-term user preferences
* Combination of different types of user preferences: tastes, interests, needs, goals, mood
* Cross-domain recommendations, based on user preferences about different interest aspects, e.g. by merging movie and music tastes
* Cross-representation recommendations, considering diverse sources of user preferences: explicit and implicit feedback
* Recommendation of resources of different nature: news, reviews, scientific papers, etc.
* Recommendation of resources belonging to different multimedia: text, image, audio, video
* Recommendation of diverse resources, e.g. according to content attributes, and user consuming behaviors
* Recommendation of resources annotated in different languages
* Contextualization of multiple user preferences, e.g. by distinguishing user preferences at work and on holidays
* Cross-context recommendations, e.g. by merging information about location, time and social aspects
* Multi-dimensional recommendation based on several contextual features, e.g. physical and social environment, device and network settings, and external events
* Multi-criteria recommendation, exploiting ratings and evaluations about multiple user/item characteristics
* Group recommendation, oriented to several users, e.g. suggesting tourist attractions to a group of friends, and suggesting a TV show to a family

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Keynote
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We are pleased to announce that Yehuda Koren, from Yahoo! Research, will be our invited speaker.

The title of his talk is "I Want to Answer, Who Has a Question? Yahoo!
Answers Recommender System."

More details in:
http://ir.ii.uam.es/hetrec2011/keynote.html

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Datasets
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In this edition of the workshop, we make available on-line datasets with heterogeneous information from several social systems.

* hetrec11-movielens-2k: a subset of the MovieLens10M dataset with its movie data merged with IMDb and Rotten Tomatoes. Main characteristics: 2.1K users, 10.2K movies, 13.2K tags, 855.6K ratings.

* hetrec11-delicious-2k: a dataset that contains social networking, bookmarking, and tagging information of a set of users from Delicious social bookmarking system. Main characteristics: 1.8K users, 69.2K bookmarks, 53.4K tags, 7.7K social relations.

* hetrec11-lastfm-2k: a dataset that contains social networking, tagging, and music listening information of a set of users from Last.fm music website. Main characteristics: 1.9K users, 17.6K artists, 11.9K tags, 186.5K listening records, 12.7K social relations.

These datasets can be used by participants to experiment and evaluate their recommendation approaches, and be enriched with additional data, which may be published at the workshop website for future use.

More details in:
http://ir.ii.uam.es/hetrec2011/datasets.html

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Organizing Committee
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* Iván Cantador, Universidad Autónoma de Madrid, Spain
* Peter Brusilovsky, University of Pittsburgh, USA
* Tsvi Kuflik, University of Haifa, Israel

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Contact information
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Contact e-mail: hetrec2011@easychair.org