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Issue No. 08 - Aug. (2012 vol. 24)
ISSN: 1041-4347
pp: 1478-1490
M. F. Hornick , Oracle Corp., Burlington, MA, USA
P. Tamayo , Oracle Corp., Burlington, MA, USA
In this paper, we describe the problem of recommending conference sessions to attendees and show how novel extensions to traditional model-based recommender systems, as suggested in Adomavicius and Tuzhilin [CHECK END OF SENTENCE], can address this problem. We introduce Recommendation Engine by CONjoint Decomposition of ITems and USers (RECONDITUS)-a technique that is an extension of preference-based recommender systems to recommend items from a new disjoint set to users from a new disjoint set. The assumption being that preferences exhibited by users with known usage behavior (e.g., past conference session attendance), which can be abstracted by projections of user and item matrices, will be similar to ones of new (different) users where the basic environment and item domain are the same (e.g., new conference). RECONDITUS requires no item ratings, but operates on observed user behavior such as past conference session attendance. The RECONDITUS paradigm consists of projections of both user and item data, and the learning of relationships in projected space. Once established, the relationships enable predicting new relationships and provide associated recommendations. The approach can encompass several traditional data mining problems where both clustering and prediction are necessary. RECONDITUS has been evaluated using data from the Oracle OpenWorld conference.
sparse matrices, collaborative filtering, data mining, matrix decomposition, recommender systems, Oracle OpenWorld conference, disjoint user-item sets, conference sessions, recommendation engine, conjoint decomposition of item and users, preference-based recommender systems, model-based recommender systems, known usage behavior, item matrices, item domain, user matrices, user behavior, RECONDITUS paradigm, data mining problem, Matrix decomposition, Recommender systems, Collaboration, Abstracts, Data models, Data mining, Sparse matrices, data mining., Recommender systems, collaborative filtering, extensions to recommender systems

M. F. Hornick and P. Tamayo, "Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 1478-1490, 2012.
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