2nd Call for Papers
2nd International Workshop on Information Heterogeneity and Fusion in
Recommender Systems (HetRec 2011)
27th October 2011 | Chicago, IL, USA
Held in conjunction with the
5th ACM Conference on Recommender Systems (RecSys 2011)
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:
* Paper submission: 25 July 2011
* Notification of acceptance: 19 August 2011
* Camera-ready version due: 12 September 2011
* HetRec 2011 Workshop: 27 October 2011
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
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.
Topics of interest
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
* 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
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:
* Iván Cantador, Universidad Autónoma de Madrid, Spain
* Peter Brusilovsky, University of Pittsburgh, USA
* Tsvi Kuflik, University of Haifa, Israel
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