A new approach for collaborative filtering based on Bayesian network inference

Abstract

Collaborative filtering (CF) is one of the most popular algorithms, for recommendation in cases, the items which are recommended to users, have been determined by relying on the outcomes done on surveying their communities. There are two main CF-approaches, which are memory-based and model-based. The model-based approach is more dominant by real-time response when it takes advantage of inference mechanism in recommendation task. However the problem of incomplete data is still an open research and the inference engine is being improved more and more so as to gain high accuracy and high speed. I propose a new model-based CF based on applying Bayesian network (BN) into reference engine with assertion that BN is an optimal inference model because BN is user's purchase pattern and Bayesian inference is evidence-based inferring mechanism which is appropriate to rating database. Because the quality of BN relies on the completion of training data, it gets low if training data have a lot of missing values. So I also suggest an average technique to fill in missing values.