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Adaptable inheritance-based prediction model for multi-criteria recommender system

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Abstract

A recommender system is an emerging personalization strategy in web applications to deal with information overload. Most recommender systems suggest items to users based on a single criterion, i.e., overall ratings. However, multi-criteria ratings are used for modeling more complex preferences of users. The incorporation of criteria ratings can lead to generating more reliable recommendations for users. Despite the success of multi-criteria methods, there is a need to be further optimized to handle popular issues, e.g., sparsity and cold-start. This research study presents a novel multi-criteria collaborative filtering recommender system based on optimization algorithms. The proposed method consists of an adaptable predictive model called multi-criteria inheritance-based prediction (MC-INH-BP). MC-INH-BP allows the customizing of the predictive model to suit the user context. Also, we propose a user profiling method called dynamic user interest print (D-UIP). The D-UIP stores the dynamic preferences of the users. The use of D-UIP reduces the impact of four challenges, critical users, tolerant users, dynamic opinion of the recurring users, and dynamic quality of the item. A set of experiments are conducted to compare MC-INH-BP with other single-criterion and multi-criteria collaborative filtering methods. The benchmark dataset, HotelExpedia, is used. The results prove the capability of MC-INH-BP to achieve better prediction accuracy regardless of the current context of the user. Besides, the results reveal that MC-INH-BP mitigates the cold start and sparsity issues.

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Authors and Affiliations

  1. Data Science Department, Princess Sumaya University for Technology, Amman, Jordan

    Bushra Alhijawi

  2. AIRC, Ajman University, Ajman, United Arab Emirates

    Salam Fraihat

  3. Mutah University, Karak, Jordan

    Arafat Awajan

Authors
  1. Bushra Alhijawi
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  2. Salam Fraihat
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  3. Arafat Awajan
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Correspondence to Bushra Alhijawi.

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Alhijawi, B., Fraihat, S. & Awajan, A. Adaptable inheritance-based prediction model for multi-criteria recommender system. Multimed Tools Appl 82, 32421–32442 (2023). https://doi.org/10.1007/s11042-023-14728-z

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