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ADC@𝜃r: Adaptive divisional categorization of ratings under rating threshold 𝜃r for similarity computation in recommendation systems

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Abstract

In the recommendation systems (RSs), it is imperative to incorporate the hidden contextual meaning of users’ provided ratings in the similarity computation. To draw such contextual meanings, existing models use the fixed categorization of accessible ratings. However, due to the excessive variation in similarly co-rated item pairs, they produce ambiguous contextual meanings that yield inconsistent results for the user pairs similarities. Therefore, to deal with this problem, this paper proposes an adaptive divisional categorization (ADC)-based RS, namely ADC@𝜃r, that obtains the optimal contextual divisions of accessible ratings under the rating threshold 𝜃r. Here, accessible ratings are the numerical scores that RS users use to express their preferences on the underlying items. A set of adaptive divisions under rating threshold 𝜃r is termed optimal if most of its rating divisions cover a large portion of rating records of a given dataset. For so, the proposed ADC@𝜃r model keeps only those divisions of ratings whose significance values are high, i.e., cover a large portion of the rating records by the ratings of these divisions. Further, the contextual mean square deviation (CMSD) model is employed to compute user pairs’ similarity using the obtained adaptive divisions of accessible ratings. The experimental results obtained on the benchmark Movielens-100K and Movielens-1M datasets justify the proposed model’s superiority over the competitive models.

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Notes

  1. The subjective information includes the rating asked from the expert users on data items, whereas the objective information consists of the general users’ preferences.

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Acknowledgements

The authors would like to thank the reviewers for their valuable suggestions.

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Authors and Affiliations

  1. Computational Intelligence and Data Mining Laboratory, ABV- Indian Institute of Information Technology and Management, Gwalior, India

    Ankush Jain, Surendra Nagar & Pramod Kumar Singh

  2. Modeling and Simulation Laboratory, ABV- Indian Institute of Information Technology and Management, Gwalior, India

    Joydip Dhar

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  1. Ankush Jain
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  2. Surendra Nagar
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Correspondence to Ankush Jain.

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Jain, A., Nagar, S., Singh, P.K. et al. ADC@𝜃r: Adaptive divisional categorization of ratings under rating threshold 𝜃r for similarity computation in recommendation systems. Appl Intell 52, 2134–2153 (2022). https://doi.org/10.1007/s10489-021-02428-w

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