A Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System

TL;DR

This paper introduces UISVD++, a matrix decomposition model that incorporates movie types and user ages into the SVD++ framework, improving prediction accuracy and addressing cold start issues in movie recommendation systems.

Contribution

The paper proposes a novel UISVD++ model that fuses item type and user age attributes into the SVD++ framework, enriching side information for better recommendations.

Findings

Outperforms baseline models in prediction accuracy.

Effectively alleviates cold start problems.

Validated on Movielens datasets.

Abstract

Currently, matrix decomposition is one of the most widely used collaborative filtering algorithms by using factor decomposition to effectively deal with large-scale rating matrix. It mainly uses the interaction records between users and items to predict ratings. Based on the characteristic attributes of items and users, this paper proposes a new UISVD++ model that fuses the type attributes of movies and the age attributes of users into SVD++ framework. By projecting the age attribute into the user's implicit space and the type attribute into the item's implicit space, the model enriches the side information of the users and items. At last, we conduct comparative experiments on two public data sets, Movielens-100K and Movielens-1M. Experiment results express that the prediction accuracy of this model is better than other baselines in the task of predicting scores. In addition, these results also show that UISVD++ can effectively alleviate the cold start situation.