Patterns (May 2020)
Global and Local Tensor Factorization for Multi-criteria Recommender System
Abstract
Summary: In multi-criteria recommender systems, matrix factorization characterizes users and items via latent factor vectors inferred from user-item rating patterns. However, two-dimensional matrix factorization models may not be able to cope with the recommendation problem that involves additional criterion-specific rating data. This study introduces a tensor factorization method to handle three-dimensional user-item-criterion rating data. Moreover, we observe that using single global tensor factorization alone may not be sufficient to characterize diverse preferences among different groups of users, and a combined global and local tensor factorization method (GLTF) for multi-criteria recommendation is thus proposed. One key benefit of the GLTF is that it can leverage global user-item-criterion rating patterns while also exploiting local user-subset specific rating behaviors to jointly infer the latent factor representations for users, items, and specific item criteria. Experimental results, which used real-life data available to the public, demonstrated that the GLTF is superior to well-established baseline methods. The Bigger Picture: We propose a global and local tensor factorization method (GLTF) to solve the multi-criteria recommendation problem commonly experienced when e-commerce systems recommend products to users based on multiple different ratings. The method uses additional criterion-specific ratings in addition to existing user-item rating data for better recommendations. It can jointly learn a global predictive model and multiple local predictive models, not only by discovering the overall structure of the entire rating tensor but also by capturing diverse rating behaviors of users in individual subtensors. The GLTF can take advantage of the user's multi-criteria rating information to discover the user's behavior, predict the information and products that the user is interested in, and obtain more accurate recommendation results. In the future, we plan to apply the GLTF in a much larger dataset for evaluation and will improve the model to mitigate the bottleneck caused by the data sparsity problem.
