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CUbRIK research at RecSys 2012


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reference research illustrated in "Swarming to Rank for Recommender Systems" publication

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CUbRIK research at RecSys 2012

  1. 1. Swarming to Rank for Recommender Systems Ernesto Diaz-Aviles, Mihai Georgescu, and Wolfgang Nejdl Overview • Address the item recommendation task in the context of recommender systems • An approach to learning ranking functions exploiting collaborative latent factors as features • Instead of manually creating an item feature vector, factorize a matrix of user-item interactions •Use these collaborative latent factors as input to the Swarm Intelligence(SI) ranking method SwarmRank SI for Recommender SystemsSwarm-RankCF Evaluation• a collaborative learning to rank algorithm based on SI• while learning to rank algorithms use hand-picked feature to Dataset: Real world data from internet radio:represent items we learn such features based on user-item 5-core of the Dataset – 1K Usersinteractions, and apply a PSO-based optimization algorithm transactions 242,103that directly maximizes Mean Average Precision. Unique users 888 Items(artists) 35,315 Evaluation Methodology: All-but-one protocol or leave-one-out holdout method where hit(u) = 1, if the hidden item I is present in u’s Top-N list of recommendations, and 0 otherwise. Contact: Ernesto Diaz-Aviles, Mihai Georgescu email: {diaz, georgescu} L3S Research Center / Leibniz Universität Hannover Appelstrasse 4, 30167 Hannover, Germany phone: +49 511 762-19715