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On improving aggregate recommendation diversity and novelty in folksonomy-based social systems

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Abstract

Benefit from technical advances in the Internet of Things, many social media applications relative to folksonomy have become ubiquitous. The size and complexity of folksonomy-based systems can unfortunately lead to information overload and reduced utility for users. Consequentially, the increasing need for recommender services from users has arisen. Many efforts have been made to address recommendation accuracy as well as other issues with respect to personalized recommendation in such systems. A key challenge facing these systems is that the most useful individual recommendations are to be found among diverse niche resources while increasing diversity most often compromises accuracy. In this paper, we introduce a simple yet elegant method—Diversity-aware Personalized PageRank (DaPPR)—to address this challenge from the aggregate perspective. DaPPR exploits a balance factor to adjust the influence of a personalized ranking vector and a unified non-personalized ranking vector based on PageRank. By this, it can reduce the impact of resource popularity on recommendations and then generate more diverse and novel recommendations to users. A hybrid DaPPR model that combines two ranking processes on the user–resource and the resource–tag bipartite graphs is specifically designed to meet the requirements in folksonomy-based systems. According to solid experiments, our proposed method yields better results balancing both aggregate accuracy and aggregate diversity (novelty). Improvements of all performance metrics are also obtained compared with the existing algorithms.

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Notes

  1. http://www.delicious.com

  2. http://www.lastfm.com

  3. http://www.imdb.com, http://www.rottentomatoes.com

  4. http://www.grouplens.org

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Acknowledgments

This work was supported by the Applied Basic Research Project of Yunnan Province (No.2013FB009) and the Scientific Research Project of Yunnan University (No.2010YB024), Special Funds for “Middle-aged and Young Core Instructor Training Program” of Yunnan University,  the National Natural Science Foundation of China (No.61070013, No.U1135005), and ”Hundred Talents Recruitment Program” of Global Experts of Hubei. This work of Jun He was supported by the National Natural Science Foundation of China (No.61203273). We are grateful to anonymous reviewers for their useful comments and suggestions which contribute to substantially improving this paper.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Hao Wu, Bo Li & Yijian Pei

  2. School of Software, Wuhan University, Wuhan, 420073, China

    Xiaohui Cui

  3. Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jun He

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  1. Hao Wu
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  2. Xiaohui Cui
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  3. Jun He
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  4. Bo Li
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  5. Yijian Pei
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Correspondence to Hao Wu, Xiaohui Cui or Jun He.

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Wu, H., Cui, X., He, J. et al. On improving aggregate recommendation diversity and novelty in folksonomy-based social systems. Pers Ubiquit Comput 18, 1855–1869 (2014). https://doi.org/10.1007/s00779-014-0785-0

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