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2011 Proceeding- Conference Chairs:
- Iván Cantador,
- Peter Brusilovsky,
- Tsvi Kuflik
The 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) was held in Chicago, IL, USA, on the 27th of October 2011, under the frame of the 5th ACM Conference on Recommender Systems (RecSys 2011).
HetRec workshop represented a meeting point for researchers and practitioners interested in addressing the challenges posed by information heterogeneity in recommender systems and studying information fusion in this context.
In this second edition, we made available on-line datasets with heterogeneous information from several social systems. These datasets could be used by participants to experiment and evaluate their recommendation approaches, and can be enriched with additional data, which may be published at the workshop website for future use. These datasets are kindly hosted by GroupLens research group at University of Minnesota.
A total of ten papers were presented at the workshop. There were six long papers, and four short papers. Fourteen submissions were received, and each of them was reviewed by two members of the Program Committee.
Proceeding Downloads
Information market based recommender systems fusion
Recommender Systems have emerged as a way to tackle the overload of information reflected in the increasing volume of information artefacts in the web and elsewhere. Recommender Systems analyse existing information on the user activities in order to ...
A kernel-based approach to exploiting interaction-networks in heterogeneous information sources for improved recommender systems
Pairwise interaction networks capture inter-user dependencies (e.g. social networks) and inter-item dependencies (e.g item categories) that provide insight into user and item behavior. It is often assumed that such interaction information is informative ...
Learning multiple models for exploiting predictive heterogeneity in recommender systems
Collaborative filtering approaches exploit information about historical affinities or ratings to predict unknown affinities between sets of "users" and "items" and make recommendations. However a model that also incorporates heterogeneous sources of ...
A generic semantic-based framework for cross-domain recommendation
In this paper, we present an ongoing research work on the design and development of a generic knowledge-based description framework built upon semantic networks. It aims at integrating and exploiting knowledge on several domains to provide cross-domain ...
Hybrid algorithms for recommending new items
Despite recommender systems based on collaborative filtering typically outperform content-based systems in terms of recommendation quality, they suffer from the new item problem, i.e., they are not able to recommend items that have few or no ratings. ...
Expert recommendation based on social drivers, social network analysis, and semantic data representation
Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple ...
Experience Discovery: hybrid recommendation of student activities using social network data
The aim of the Experience Discovery project is to recommend extracurricular activities to high school and middle school students in urban areas. In implementing this system, we have been able to make use of both usage data and data drawn from a social ...
Personalizing tags: a folksonomy-like approach for recommending movies
Movie recommender systems attempt to find movies which are of interest for their users. However, as new movies are added, and new users join movie recommendation services, the problem of recommending suitable items becomes increasingly harder. In this ...
Personalized pricing recommender system: multi-stage epsilon-greedy approach
Many e-commerce sites use recommender systems, which suggest items that customers prefer. Though recommender systems have achieved great success, their potential is not yet fulfilled. One weakness of current systems is that the actions of the system ...
Matrix co-factorization for recommendation with rich side information and implicit feedback
Most recommender systems focus on the areas of leisure activities. As the Web evolves into omnipresent utility, recommender systems penetrate more serious applications such as those in online scientific communities. In this paper, we investigate the ...
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