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Personalized recommendation: From clothing to academic

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Abstract

Information retrieval is useful in all aspects of life, ranging from clothing shopping to education and academic pursuits. Many systems optimize models with pairwise ranking techniques such as Bayesian Personalized Ranking (BPR) for personalized information retrieval. A Bayesian personalized ranking system can assist specific shoppers, students, and researchers based on their interaction, which has illustrated an enormous capability for improvement by generating intelligent recommendations, such as clothing, books, and other related information. However, for such users, finding the desired clothing and books online is complex and is influenced by various factors (e.g., visual appearance and time). As such, traditional personalized recommendation methods that model only user-product interaction data would deliver unsatisfactory recommendation results. In this paper, we propose combining visual, temporal, and sequential information for personalized recommendations. Technically speaking, our main contributions include: (1) We incorporate the image features of clothing and books into personalized ranking to model users’ preferences. (2) We design a new time model for personalized recommender systems. The visual features are then injected into the time model to capture the temporal dynamics of visual preferences. (3) To this end, we present a Time Hierarchical Embedding (T-Sherlock) approach, which can incorporate sequential and temporal information simultaneously to model users’ preferences for different categories of products. To reduce the impact of adversarial noise, we train a T-Sherlock objective function using minimax adversarial training (AT-Sherlock). Experiments on real-world datasets demonstrated the efficacy of our methods in comparison to baselines.

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Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Agyemang Paul, Zhefu Wu, Kai Liu & Shufeng Gong

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  1. Agyemang Paul
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  2. Zhefu Wu
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  3. Kai Liu
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  4. Shufeng Gong
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Correspondence to Zhefu Wu.

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Paul, A., Wu, Z., Liu, K. et al. Personalized recommendation: From clothing to academic. Multimed Tools Appl 81, 14573–14588 (2022). https://doi.org/10.1007/s11042-022-12259-7

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