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Group recommendation exploiting characteristics of user-item and collaborative rating of users

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Abstract

Recommender Systems have gained popularity in recent years due to their ability to expedite users’ selection processes quickly. Traditional recommender systems mainly focus on providing recommendations to a user. However, It is not a suitable recommendation technique for groups of users. A group recommendation system (GRS) addresses this issue of recommendation. GRS is popular in domain, such as health, tourism, movies, etc. A few research is reported in the GRS domain that satisfy each user requirement in a group. The task of GRS can be divided into three subtasks: the formation of the group, rating prediction of individual members in a group, and aggregating them. The state of art technique can not adequately address the issue of group satisfaction. To maximize member satisfaction, we exploit the cluster validation metrics to form suitable groups of users in this paper. We propose a novel technique for rating the prediction of individual members in a group on an item considering the user’s characteristics, such as age, gender, and occupation. A Novel aggregation function named Tendency-based Aggregation (TA) is proposed for aggregating the predicted rating of an individual in a group. We conducted the experiments on datasets ML-1M-I, ML-1M-II, and ML-100k to show the efficiency of the proposed method. To validate the proposed approach, we utilize popular evaluation methods used in GRS, such as MAE, RMSE, and group satisfaction metric (GSM). We also report the result of proposed GRS utilizing the newly introduced group satisafction error (SEG). The experimental outcomes show that the proposed method outperforms all the existing methods. The proposed approach improves the GSM by at most 35% compared to the state-of-the-art.

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Notes

  1. https://machinelearningmastery.com/training-validation-test-split-and-cross-validation-done-right/

  2. https://grouplens.org/datasets/movielens/

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Authors and Affiliations

  1. Computer Science and Engineering, National Institute of Technology Rourkela, Sector 1, Rourkela, 769008, Odisha, India

    Jitendra Kumar & Bibhudatta Sahoo

  2. Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, 221005, Uttar Pradesh, India

    Bidyut Kumar Patra

  3. Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing Kurnool, Jagannathagattu, Kurnool, 518007, Andhra Pradesh, India

    Korra Sathya Babu

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  1. Jitendra Kumar
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Kumar, J., Patra, B.K., Sahoo, B. et al. Group recommendation exploiting characteristics of user-item and collaborative rating of users. Multimed Tools Appl 83, 29289–29309 (2024). https://doi.org/10.1007/s11042-023-16799-4

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