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Item Refinement for Improved Recommendations

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Digital Connectivity – Social Impact (CSI 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 679))

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Abstract

Recommender systems serve as business tools which make use of knowledge discovery techniques to reshape the world of E-Commerce. Collaborative filtering (CF), the most effective type of recommender systems, predicts user preferences by learning from past user-item relationships. Prediction algorithms are based on similarity between item vectors or user profiles. However similarity computations become less efficient if item vectors or user profiles do not contain enough ratings. A technique which is based on Pseudo Relevance Feedback is proposed to expand item vectors in order to make them contain more ratings. The proposed approach first expands item profiles and refines the expansion in order to remove expansion deviations. The experiments on MovieLens data set show that the proposed technique is efficient in expanding the rating matrix and outperforms state of the art collaborative filtering techniques for providing more efficient predictions.

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Authors and Affiliations

  1. Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, India

    R. Latha & R. Nadarajan

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  1. R. Latha
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  2. R. Nadarajan
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Correspondence to R. Latha .

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Editors and Affiliations

  1. Karpagam Academy of Higher Education, Coimbatore, India

    S. Subramanian

  2. PSG College of Technology, Coimbatore, India

    R. Nadarajan

  3. International Institute of Information Technology, Bengaluru, Karnataka, India

    Shrisha Rao

  4. Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, Tamil Nadu, India

    Shina Sheen

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Latha, R., Nadarajan, R. (2016). Item Refinement for Improved Recommendations. In: Subramanian, S., Nadarajan, R., Rao, S., Sheen, S. (eds) Digital Connectivity – Social Impact. CSI 2016. Communications in Computer and Information Science, vol 679. Springer, Singapore. https://doi.org/10.1007/978-981-10-3274-5_7

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  • DOI: https://doi.org/10.1007/978-981-10-3274-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3273-8

  • Online ISBN: 978-981-10-3274-5

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