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In this talk the design and implementation of an item-item recommender (IIR) based on linear algebra operations are presented. The presented IIR has great performance and scalability properties. Design and algorithmic approaches are discussed for recommendation proofs, tuning, and diversification. Recommendations of movies, music, and houses will be demonstrated using a common user interface.
The algorithms discussed are from the fields of sparse matrix linear algebra, collaborative filtering, natural language processing, principal component analysis, association rule learning, and outlier detection.
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