Recommendation in evolving online networks

Recommender system is an effective tool to find the most relevant information for online users. By analyzing the historical selection records of users, recommender system predicts the most likely future links in the user-item network and accordingly constructs a personalized recommendation list for...

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Bibliographic Details
Published in:The European physical journal. B, Condensed matter physics Condensed matter physics, 2016-02, Vol.89 (2), p.1-7, Article 46
Main Authors: Hu, Xiao, Zeng, An, Shang, Ming-Sheng
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Complex Systems
Condensed Matter Physics
Evolution
Fluid- and Aerodynamics
Networks
On-line systems
Physics
Physics and Astronomy
Recommender systems
Regular Article
Solid State Physics
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Summary:Recommender system is an effective tool to find the most relevant information for online users. By analyzing the historical selection records of users, recommender system predicts the most likely future links in the user-item network and accordingly constructs a personalized recommendation list for each user. So far, the recommendation process is mostly investigated in static user-item networks. In this paper, we propose a model which allows us to examine the performance of the state-of-the-art recommendation algorithms in evolving networks. We find that the recommendation accuracy in general decreases with time if the evolution of the online network fully depends on the recommendation. Interestingly, some randomness in users’ choice can significantly improve the long-term accuracy of the recommendation algorithm. When a hybrid recommendation algorithm is applied, we find that the optimal parameter gradually shifts towards the diversity-favoring recommendation algorithm, indicating that recommendation diversity is essential to keep a high long-term recommendation accuracy. Finally, we confirm our conclusions by studying the recommendation on networks with the real evolution data.
ISSN:1434-6028
1434-6036
DOI:10.1140/epjb/e2016-60509-9