Latent Feature Based FM Model For Rating Prediction

TL;DR

This paper introduces two novel feature-based Factorization Machine models that incorporate implicit feedback and watching order information to improve movie rating prediction accuracy.

Contribution

It proposes two new models, Topic-based FM and Vector-based FM, leveraging LDA and word2vec for enhanced latent feature extraction in rating prediction.

Findings

Vector-based FM outperforms baseline models

Implicit feedback improves rating prediction accuracy

Order information enhances model performance

Abstract

Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM). However, traditional matrix factorization methods fail to utilize the benefit of implicit feedback, which has been proved to be important in Rating Prediction problem. In this work, we consider a specific situation, movie rating prediction, where we assume that watching history has a big influence on his/her rating behavior on an item. We introduce two models, Latent Dirichlet Allocation(LDA) and word2vec, both of which perform state-of-the-art results in training latent features. Based on that, we propose two feature based models. One is the Topic-based FM Model which provides the implicit feedback to the matrix factorization. The other is the Vector-based FM Model which expresses the order info of watching history. Empirical results on three datasets demonstrate that our method performs better than the baseline model and confirm that Vector-based FM Model usually works better as it contains the order info.