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A multistep priority-based ranking for top-N recommendation using social and tag information

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Abstract

Recommender system is a collection of information retrieval tools and techniques used for recommending items to users based on their choices. For improving recommendation accuracy, the use of extra information (e.g., social, trust, item tags, etc.) other than user-item rating data remains an active area of research since last one and half decade. In this paper, we propose a novel methodology for top-N item recommendation, which uses three different kinds of information: user-item rating data, social network among the users, and tags associated with the items. The proposed method has mainly five steps: (i) creation of neighbor users’ item set, (ii) construction of the user-feature matrix, (iii) computation of user priority, (iv) computation of item priority, and finally, (v) recommendation based on the item priority. We implement the proposed methodology with three recommendation dataset. We compare our results with that of the obtained from some state-of-the-art ranking methods and observe that recommendation accuracy is improved in the case of the proposed algorithm for both all users and cold-start users scenarios. The algorithm is also able to generate more cold-start items in the recommended item list.

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Notes

  1. In this paper, we use tag and feature, interchangeably

  2. In this paper, we have used the term architecture and methodology, interchangeably

  3. https://www.last.fm/

  4. https://del.icio.us/about

  5. https://www.librarything.com/

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Funding

This study is funded by MHRD [E-Business Centre of Excellence (Grant No. F.No.5-5/2014-TS.VII.)] and IIT Gandhinagar (Project No. MIS/IITGN/PD-SCH/201415/006).

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  1. Suman Banerjee, Pratik Banjare and Bithika Pal contributed equally in this work.

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, India

    Suman Banerjee

  2. Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Pratik Banjare & Mamata Jenamani

  3. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Bithika Pal

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  1. Suman Banerjee
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  2. Pratik Banjare
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Correspondence to Suman Banerjee.

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The major part of this work has been done when the first author was a PhD student at Indian Institute of Technology, Kharagpur and was supported by the grant E-Business Centre of Excellence (Grant No. F.No.5-5/2014-TS.VII.). Currently, the first author is supported by the Post Doctoral Fellowship Grant sponsored by Indian Institute of Technology, Gandhinagar (Project No. MIS/IITGN/PD-SCH/201415/006).

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Banerjee, S., Banjare, P., Pal, B. et al. A multistep priority-based ranking for top-N recommendation using social and tag information. J Ambient Intell Human Comput 12, 2509–2525 (2021). https://doi.org/10.1007/s12652-020-02388-y

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