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Empowering reciprocal recommender system using contextual bandits and argumentation based explanations

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Abstract

Reciprocal Recommender Systems (RRS) aim to recommend relevant matches to users based on the mutual agreement of their preferences. Explainability of reciprocal recommendations is important for developing a persuasive reciprocal recommender system, since it can improve the effectiveness and credibility of the reciprocal recommendation results. Explainable RRS provide an explanation highlighting why a recommendation would be relevant to the user. Explaining the rationale behind predictions with textual or visual artifacts help in increasing trustworthiness and transparency of the system which is crucial especially for models that are used in critical decision making. In this work, XSiameseBiGRU-UCB, a deep learning contextual bandits framework with post-hoc argumentation based explanations for RRS is proposed. XSiameseBiGRU-UCB is an explainable Siamese neural network-based framework that provides explanations to justify the generated reciprocal recommendations for both the parties involved. In RRS, dilemma between exploitation and exploration requires identifying the best possible recommendation from known information or collecting more information about the environment while generating reciprocal recommendations. To tackle this, we propose to use a contextual bandit policy with upper confidence bound, which adaptively exploits and explores user interests to achieve increased rewards in the long run. Experimental studies conducted with four real-world datasets validate the efficacy of the proposed approach.

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Data Availability

No datasets were generated. Datasets used in the current study are publicly available.

Notes

  1. https://www.kaggle.com/datasets/annavictoria/speed-dating-experiment

  2. https://figshare.com/articles/dataset/Gender-specific_preference_in_online_dating/6429443/1

  3. https://www.kaggle.com/datasets/andrewmvd/okcupid-profiles

  4. https://insights.stackoverflow.com/survey, https://www.kaggle.com/samrat77/indeed-software-engineer-job-dataset

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Funding

*No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Delhi, 110007, Delhi, India

    Tulika Kumari & Punam Bedi

  2. Shaheed Rajguru College of Applied Sciences for Women, University of Delhi, 110096, Delhi, India

    Tulika Kumari

  3. Keshav Mahavidyalaya, University of Delhi, 110034, Delhi, India

    Bhavna Gupta

  4. P.G.D.A.V. College, University of Delhi, 110065, Delhi, India

    Ravish Sharma

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  1. Tulika Kumari
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  2. Bhavna Gupta
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  4. Punam Bedi
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Contributions

Ms Tulika Kumari: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Roles/Writing - original draft, Writing - review and editing. Dr. Bhavna Gupta: Conceptualization, Methodology, Supervision, Review, Editing, Project Administration, Resources, Validation Dr. Ravish Sharma: Conceptualization, Methodology, Supervision, Review, Editing, Project Administration, Resources, Validation Prof. Punam Bedi: Conceptualization, Methodology, Supervision, Review, Editing, Project Administration, Resources, Validation

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Correspondence to Bhavna Gupta.

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Kumari, T., Gupta, B., Sharma, R. et al. Empowering reciprocal recommender system using contextual bandits and argumentation based explanations. World Wide Web 26, 2969–3000 (2023). https://doi.org/10.1007/s11280-023-01173-z

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