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Pruning trust–distrust network via reliability and risk estimates for quality recommendations

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Abstract

The efficacy of trust links from social networks in boosting the user inter-connectivity in an otherwise poorly connected user group, obtained from historical preference data, has recently led to adoption of systems exploiting both these information sources to discover user proximities for recommender systems (RS). However, the investigation into the utility of distrust in the recommendation process is in its infancy. We propose a collaborative filtering framework based on computing user trust by exploiting functional and referral trust and distrust information together with user preference data. The inclusion of multiple sources of opinions for computing trust results in improved coverage and the trust network so formed can be used to infer indirect trust between entities by exploiting transitivity of trust. We also quantify the risk in relying on trust statements as a function of knowledge contained in the statement and the conflict in opinions about an entity and argue that pruning the trust graph by discarding risky and retaining reliable trust statements results in more accurate and robust recommendations while not compromising on the coverage. The experimental results corroborate our ideas and outperform several baseline algorithms.

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Authors and Affiliations

  1. CMR Institute of Management Studies, No. 2, 3rd ‘C’ Cross, 6th ‘A’ Main, 2nd Block HRBR Layout, Bangalore, 560043, Karnataka, India

    Deepa Anand

  2. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    K. K. Bharadwaj

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  1. Deepa Anand
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  2. K. K. Bharadwaj
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Correspondence to Deepa Anand.

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Anand, D., Bharadwaj, K.K. Pruning trust–distrust network via reliability and risk estimates for quality recommendations. Soc. Netw. Anal. Min. 3, 65–84 (2013). https://doi.org/10.1007/s13278-012-0049-9

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  • DOI: https://doi.org/10.1007/s13278-012-0049-9

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