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Recommendations with Sparsity Based Weighted Context Framework

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10963))

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Abstract

Context-Aware Recommender Systems (CARS) is a sort of information filtering tool which has become crucial for services in this big era of data. Owing to its characteristic of including contextual information, it achieves better results in terms of prediction accuracy. The collaborative filtering has been proved as an efficient technique to recommend items among all existing techniques in this area. Moreover, incorporation of other evolutionary techniques in it for contextualization and to alleviate sparsity problem can give an additive advantage. In this paper, we propose to find the vector of weights using particle swarm optimization to control the contribution of each context feature. It is aimed to make a balance between data sparsity and maximization of contextual effects. Further, the weighting vector is used in different components of user and item neighborhood-based algorithms. Moreover, we present a novel method to find aggregated similarity from local and global similarity based on sparsity measure. Local similarity gives importance to co-rated items while global similarity utilizes all the ratings assigned by a pair of users. The proposed algorithms are evaluated for Individual and Group Recommendations. The experimental results on two contextually rich datasets prove that the proposed algorithms outperform the other techniques of this domain. The sparsity measure that is best suited to find aggregation is dataset dependent. Finally, the algorithms show their efficacy for Group Recommendations too.

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Authors and Affiliations

  1. Atma Ram Sanatan Dharam College, University of Delhi, Delhi, India

    Veer Sain Dixit & Parul Jain

Authors
  1. Veer Sain Dixit
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  2. Parul Jain
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Correspondence to Parul Jain .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Dixit, V.S., Jain, P. (2018). Recommendations with Sparsity Based Weighted Context Framework. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10963. Springer, Cham. https://doi.org/10.1007/978-3-319-95171-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-95171-3_23

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