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