Effect of user tastes on personalized recommendation

TL;DR

This paper explores how user tastes influence personalized recommendation algorithms, proposing a tunable model that adjusts recommendation power based on user preferences and object popularity, improving accuracy especially in sparse data scenarios.

Contribution

Introduces a tunable parameter in a mass-diffusion recommendation algorithm to incorporate user tastes, enhancing recommendation accuracy across different data densities.

Findings

Algorithm improves accuracy as measured by average ranking score.

In sparse data, recommend objects with degrees close to user tastes.

In dense data, recommend objects with degrees different from user tastes.

Abstract

In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their degree and the users' tastes. By introducing a tunable parameter, the user taste effects on the configuration of initial recommendation power distribution are investigated. The numerical results indicate that the presented algorithm could improve the accuracy, measured by the average ranking score, more importantly, we find that when the data is sparse, the algorithm should give more recommendation power to the objects whose degrees are close to the users' tastes, while when the data becomes dense, it should assign more power on the objects whose degrees are significantly different from user's tastes.