Improving information filtering via network manipulation

TL;DR

This paper proposes a network manipulation strategy that enhances recommender system performance by adding virtual connections to user-item bipartite networks, improving accuracy, diversity, and novelty especially for small degree items.

Contribution

It introduces a novel approach of directly modifying user-item networks to improve existing recommendation algorithms, addressing limitations like the cold-start problem.

Findings

Significant improvement in recommendation accuracy for small degree items

Enhanced diversity and novelty in recommendations

Effective on benchmark datasets MovieLens and Netflix

Abstract

Recommender system is a very promising way to address the problem of overabundant information for online users. Though the information filtering for the online commercial systems received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e. low recommendation accuracy for the small degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improve the recommendation performance. Specifically, it not only improve the recommendations accuracy (especially for the small degree items), but also help the recommender systems generate more diverse and novel recommendations.