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Improving group recommendation using deep collaborative filtering approach

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Abstract

The recommender system’s objective is to recommend items to users. Providing personalized suggestions to users is a challenging task. The traditional recommendation system recommends items for individual users. However, in recent times the popularity of group activities has increased, like watching movies with family, holiday trips with friends, etc. While predicting the rating information, existing methods disregard the metadata information. Therefore, the proposed approach used metadata information for prediction. It addresses data sparsity. This paper presents a rating prediction for group that leverages multilayer perceptron and General Matrix Factorization using metadata with Neural Collaborative Filtering techniques. The proposed approach is discussed in two steps. The first step is to learn from group-item interaction, perform one hot encoding for group and item, and then utilize this information to perform dot product by applying the GMF layer. In the second step, learn group-item interactions using group metadata and item metadata. This information is concatenated using the MLP layer. Finally, the GMF and MLP layer combined to get the final prediction ratings. The proposed approach has the advantage uses metadata to alleviate the cold start issue in group recommendation scenarios.

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Data availability

The datasets used in our experiment as https://grouplens.org/datasets/movielens/100k/ https://grouplens.org/datasets/movielens/1 m/

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Authors and Affiliations

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

    V. Ramanjaneyulu Yannam, Jitendra Kumar & Bibhudatta Sahoo

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

    Korra Sathya Babu

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  1. V. Ramanjaneyulu Yannam
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Yannam, V.R., Kumar, J., Babu, K.S. et al. Improving group recommendation using deep collaborative filtering approach. Int. j. inf. tecnol. 15, 1489–1497 (2023). https://doi.org/10.1007/s41870-023-01205-x

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