Improved personalized recommendation based on a similarity network

Ximeng Wang, Yun Liu and Fei Xiong

Physica A: Statistical Mechanics and its Applications, 2016, vol. 456, issue C, 271-280

Abstract: A recommender system helps individual users find the preferred items rapidly and has attracted extensive attention in recent years. Many successful recommendation algorithms are designed on bipartite networks, such as network-based inference or heat conduction. However, most of these algorithms define the resource-allocation methods for an average allocation. That is not reasonable because average allocation cannot indicate the user choice preference and the influence between users which leads to a series of non-personalized recommendation results. We propose a personalized recommendation approach that combines the similarity function and bipartite network to generate a similarity network that improves the resource-allocation process. Our model introduces user influence into the recommender system and states that the user influence can make the resource-allocation process more reasonable. We use four different metrics to evaluate our algorithms for three benchmark data sets. Experimental results show that the improved recommendation on a similarity network can obtain better accuracy and diversity than some competing approaches.

Keywords: Similarity network; Resource-allocation; Network-based inference; Bipartite graphs; Personalized recommendation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:456:y:2016:i:c:p:271-280

DOI: 10.1016/j.physa.2016.03.070

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