Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

Liang Zhang,Quanshen Wei,Lei Zhang,Baojiao Wang,Wen-Hsien Ho

doi:10.3390/app10041257

Abstract

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

Highlights

The advent of the Internet has exacerbated the information overload problem, which increasingly complicates the retrieval of useful information from the Internet
To address the above problems, this study proposes a novel diversity optimization method: Diversity Balancing for Two-Stage collaborative filtering (DBTS)
We use the well-known metrics of Precision and Recall to evaluate the accuracy of the algorithm

Summary

Introduction

The advent of the Internet has exacerbated the information overload problem, which increasingly complicates the retrieval of useful information from the Internet. A recommender system (RS) [1,2,3] is a powerful tool for solving the information overload problem in many online applications, and the use of RS has steadily increased in recent years. Current RS methods can be classified as content-based filtering methods [4,5] and collaborative filtering methods [6,7,8]. Content-based RSs generate recommendations after analyzing item descriptions and user interest profiles, whereas collaborative filtering RSs recommend items that are of particular interest to the active user by identifying similarities between users or between items. Collaborative filtering is among the most effective and widely used RS methods in many domains due to its easy implementation, e.g., it does not require specific domain knowledge

Methods

Results

Discussion

Conclusion