Initializing Matrix Factorization Methods on Implicit Feedback Databases
Balazs Hidasi (Gravity R&D, Hungary)
Domonkos Tikk (Gravity R&D, Hungary)
Abstract: The implicit feedback based recommendation problem--when only the user history is available but there are no ratings--is a much harder task than the explicit feedback based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Recently, implicit feedback problem is being received more attention, as application oriented research gets more attractive within the field. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the feature vectors before training. We present a general initialization framework that preserves the similarity between entities (users/items) when creating the initial feature vectors, where similarity is defined using e.g. context or metadata information. We demonstrate how the proposed initialization framework can be coupled with MF algorithms. We experiment with various similarity functions, different context and metadata based similarity concepts. The evaluation is performed on two implicit variants of the MovieLens 10M dataset and four real life implicit databases. We show that the initialization significantly improves the performance of the MF algorithms by most ranking measures.
Keywords: contextual information, implicit feedback, initialization, recommender systems, similarity