SERecSys 2017 - 2nd ICDM Workshop on Semantics-Enabled Recommender Systems

A recommender system is designed to suggest items that are expected to interest a user. In order to filter the items and produce the recommendation, Data Mining techniques are largely employed. Among the most popular recommendation approaches in the literature and in real-world applications (e.g., e-commerce websites) are the so-called content-based recommender systems. Content-based recommender systems suggest to users items that are similar to those they previously evaluated [1]. The early systems used relatively simple retrieval models, such as the Vector Space Model, with the basic TF-IDF weighting.
Simple (word-based) interest descriptions may fall short both because of semantic ambiguity and because they lack of generality. Recently, content-based recommender systems evolved and started employing external knowledge sources (e.g., ontologies) to improve accuracy and scope of recommendations [1], [2].
More recent approaches have been based on deep learning [3]. Other approaches, such as [4], have employed word embeddings in the recommendation process. Among the best known and high-performance implementations following these lines of research we mention Google’s word2vec.
Moreover, semantic technologies will soon find a connection with cognitive computing [5], cooperating in the definition of the so-called cognitive recommender systems. Given the rapid advances of Semantic Technologies, there is still a large number of options for recommender systems to take advantage of semantics.
Our workshop will solicit contributions in all topics related to employing Semantic Technologies in Recommender Systems, focused (but not limited) to the following list:
Novel approaches to user profiling in recommender systems that model behavior with semantic technologies;
Cognitive recommender systems;
Content-based recommendation algorithms that employ novel uses of semantic technologies;
Recommendation explanation using semantic technologies;
Generation of novel, diverse, and serendipitous recommendations using semantic technologies;
Hybrid recommender systems that combine semantic technologies with other recommendation techniques (e.g, collaborative);
Group-based approaches that use semantic technologies to describe the group preferences or to generate recommendations.