User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation

TL;DR

This paper proposes a user profile feature-based method to mitigate the cold start problem in collaborative filtering for movie recommendations, achieving an 8.4% accuracy improvement using the MovieLens dataset.

Contribution

The authors introduce a novel approach that uses user feature-scores derived from ratings to optimize prediction algorithms, addressing cold start issues in collaborative filtering.

Findings

Achieved 8.4% improvement over baseline collaborative filtering.

Validated approach on MovieLens 100k dataset.

Potential for generalization to other domains.

Abstract

A huge amount of user generated content related to movies is created with the popularization of web 2.0. With these continues exponential growth of data, there is an inevitable need for recommender systems as people find it difficult to make informed and timely decisions. Movie recommendation systems assist users to find the next interest or the best recommendation. In this proposed approach the authors apply the relationship of user feature-scores derived from user-item interaction via ratings to optimize the prediction algorithm's input parameters used in the recommender system to improve the accuracy of predictions when there are less past user records. This addresses a major drawback in collaborative filtering, the cold start problem by showing an improvement of 8.4% compared to the base collaborative filtering algorithm. The user-feature generation and evaluation of the system is carried out using the 'MovieLens 100k dataset'. The proposed system can be generalized to other domains as well.