Behavior patterns of online users and the effect on information filtering

Abstract

Understanding the structure and evolution of web-based user-object bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus on investigating the patterns of online users' behavior and the effect on recommendation process. Empirical analysis on the e-commercial systems show that users have significant taste diversity and their interests for niche items highly overlap. Additionally, recommendation process are investigated on both the real networks and the reshuffled networks in which real users' behavior patterns can be gradually destroyed. Our results shows that the performance of personalized recommendation methods is strongly related to the real network structure. Detail study on each item shows that recommendation accuracy for hot items is almost maximum and quite robust to the reshuffling process. However, niche items cannot be accurately recommended after removing users' behavior patterns. Our work also is meaningful in practical sense since it reveals an effective direction to improve the accuracy and the robustness of the existing recommender systems.

8 pages, 6 figures

References in corpus (12)

• Link Prediction in Complex Networks: A Survey
• Modeling the scaling properties of human mobility
• Prominence and control: The weighted rich-club effect
• Diffusion of scientific credits and the ranking of scientists
• Heat Conduction Process on Community Networks as a Recommendation Model
• Information filtering via biased heat conduction
• Local structure of directed networks
• Information filtering via preferential diffusion
• Information Filtering via Self-Consistent Refinement
• Exact Solution for the Time Evolution of Network Rewiring Models
• An improved HeatS+ProbS hybrid recommendation algorithm based on heterogeneous initial resource configurations
• Dynamics on Spatial Networks and the Effect of Distance Coarse Graining

Cited by in corpus (14)

• Recommender Systems
• Query expansion techniques for information retrieval: A survey
• An agent-based model for emotion contagion and competition in online social media
• Extracting the information backbone in online system
• Evaluating user reputation in online rating systems via an iterative group-based ranking method
• Anchoring Bias in Online Voting
• Promoting cold-start items in recommender systems
• Strong ties promote the epidemic prevalence in susceptible-infected-susceptible spreading dynamics
• A trust-based recommendation method using network diffusion processes
• An improved HeatS+ProbS hybrid recommendation algorithm based on heterogeneous initial resource configurations
• Improving information filtering via network manipulation
• Spectral coarse graining for random walk in bipartite networks
• Adaptive social recommendation in a multiple category landscape
• Membership in social networks and the application in information filtering