Graph Regularized Tensor Decomposition for Recommender Systems

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Humans make decisions when presented with choices based on influences. The Internet today presents people with abundant choices to choose from. Recommending choices with an emphasis on people's preferences has become increasingly sought. Grundy (1979), the first computer librarian Recommender System (RS), provided users with book recommendations. Growing volumes of user data in the '90s saw increased usage of commercially available RS for e-commerce, music, movies, books, and social networking services. Due to their effectiveness in providing recommendations, Collaborative Filtering (CF) algorithms are predominantly used to build these RS. However, traditional CF algorithms adopting Matrix Factorization (MF) and Nearest Neighbor (NN) methods suffer from handling sparse data or model scalability. With exponentially increasing sparse data, building scalable and accurate RS models is of focus.

This thesis uses tensors and graphs to represent available data. Emphasis is given to capturing higher-order interactions present between the data. The use of tensors is motivated as matrices cannot capture data with higher-order relations, such as variation of user ratings to items with time. The transition to using tensors has been promising with the development of efficient tensor decomposition methods and powerful machines. Graphs can capture the correlation between different entities, providing additional information intrinsic to the underlying graph structure. A Graph Regularized CANDECOMP/PARAFAC (GRCP) tensor decomposition model framework is proposed in this thesis. The thesis highlights how to graph Laplacian regularizers (GLRs) benefit CP tensor decomposition methods to build RS. The model framework is evaluated with the MovieLens data set. The model records lesser Normalized Mean Squared Error (NMSE) values than those reported in the literature. The combination of varied data sources notably aids in overcoming the drawbacks of current RS models, offering scalability with computational efficiency in linear time.