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A recommender system based on collaborative filtering, graph theory using HMM based similarity measures

Author

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  • Anshul Gupta

    (NMIMS University)

  • Pravin Srinath

    (NMIMS University)

Abstract

Collaborative filtering (CF) is a widely used method in recommendation systems (RS). In the CF, similar users' interests and preferences are analyzed and items that they might be interested in are recommended. However, the RS suggests items without maintaining the proper order (sequence) assuming that in the next purchasing user may select any single item from the list of recommended items. This limits the scope of the RS to a single future event, which does not match with the natural behavior of the users who generally purchase multiple items together in a specific sequence. Therefore in this paper, a more realistic RS is proposed which suggests a list of items in a proper sequence that users may purchase together. Since making such RS also required to measure the similarity among the users considering their sequential (temporal) purchasing behavior. The temporal similarity among the users could be calculated using a graph-based approach. However, this could lead to an NP-complete computational problem. Hence, to overcome this limitation, we also proposed an HMM-based approach that utilized the EMISSION Matrix to estimate the sequential similarity among the users. The EMISSION Matrix used with the proposed algorithm is a part of HMM (Hidden Markov Model). In HMM the EMISSION matrix is used to estimate the possibility of emission of a particular symbol from a process. In the presented work, the process is mapped with the user and the symbol with the selected item. Matching the EMISSION matrix of users' item selection process provides an improved similarity measure which also includes the sequential preferences. The performance of the proposed approach is measured for different configurations using the MovieLens dataset and several evaluation metrics. The proposed approach also overcomes the limitations of the previous algorithm which restricts the prediction domain to the user’s browsing history only.

Suggested Citation

  • Anshul Gupta & Pravin Srinath, 2022. "A recommender system based on collaborative filtering, graph theory using HMM based similarity measures," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 533-545, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01537-6
    DOI: 10.1007/s13198-021-01537-6
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    References listed on IDEAS

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    1. Chenbin Dou & Lan Zheng & Mohammad Shabaz, 2021. "Corrigendum to “Evaluation of Urban Environmental and Economic Coordination Based on Discrete Mathematical Model”," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-1, August.
    2. Chirag Sharma & Amandeep Bagga & Bhupesh Kumar Singh & Mohammad Shabaz, 2021. "A Novel Optimized Graph-Based Transform Watermarking Technique to Address Security Issues in Real-Time Application," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-27, April.
    3. Manik Rakhra & Ramandeep Singh & Tarun Kumar Lohani & Mohammad Shabaz, 2021. "Metaheuristic and Machine Learning-Based Smart Engine for Renting and Sharing of Agriculture Equipment," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, February.
    4. Chenbin Dou & Lan Zheng & Wenjuan Wang & Mohammad Shabaz & Dr. Dilbag Singh, 2021. "Evaluation of Urban Environmental and Economic Coordination Based on Discrete Mathematical Model," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, May.
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