SERecSys 2016 - 2016 Workshop on Semantics-Enabled Recommender Systems (SERecSys)

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.
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;
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.
Accepted papers will be included in the IEEE ICDM 2016 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. The workshop proceedings will be in a CD separated from the CD of the main conference. The CD is produced by IEEE Conference Publishing Services (CPS).
References
[1] Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro, “Semantics-aware content-based recommender systems,” in Recommender Systems Handbook, pp. 119?159. Springer US, 2015.
[2] Michel Capelle, Frederik Hogenboom, Alexander Hogenboom, and Flavius Frasincar, “Semantic news recommendation using wordnet and bing similarities,” in Proceedings of the 28th Annual ACM Symposium on Applied Computing, New York, NY, USA, 2013, pp. 296?302, ACM.
[3] Tomas Mikolov, Quoc V. Le, and Ilya Sutskever, “Exploiting similarities among languages for machine translation,” CoRR, vol. abs/1309.4168, 2013.
[4] Cataldo Musto, Giovanni Semeraro, Marco de Gemmis, and Pasquale Lops, “Learning word embeddings from wikipedia for content-based recommender systems,” in Advances in Information Retrieval - 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings. 2016, vol. 9626 of Lecture Notes in Computer Science, pp. 729?734, Springer.