Diversity recommendations based on user preference classification

Qingxiong Tang; Lisheng Zhang

doi:10.1117/12.3037689

27 September 2024 Diversity recommendations based on user preference classification

Qingxiong Tang, Lisheng Zhang

Author Affiliations +

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132750V (2024) https://doi.org/10.1117/12.3037689
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

Diversified recommendations can alleviate issues such as filter bubbles and redundant recommendations in recommendation systems. However, the enhancement of diversity is often accompanied by a decrease in relevance. One commonly used method for diversified recommendations is re-ranking, with most re-ranking algorithms aiming to balance the diversity of the list and its relevance to the user. However, these algorithms often overlook the user's acceptance of diversity and ignore hidden information in the ranking scores generated during the candidate list generation stage. To address these limitations, we propose the UPCC (User Preference Cluster Classification) framework to optimize existing re-ranking models for diversity recommendations. UPCC utilizes long short-term neural networks to predict the user's relevance demand for items in the next moment. It filters candidate clusters based on this demand and re-ranks the filtered clusters to obtain a set of recommended clusters. Finally, the highest-ranked items from each recommended cluster are selected to form the recommendation list. Through experiments conducted on ML-1M and ML- 100k datasets, the results indicate that the re-ranking model using UPCC improves the speed and accuracy of recommendations. Additionally, it generates recommendations that better align with users' diverse preferences.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingxiong Tang and Lisheng Zhang "Diversity recommendations based on user preference classification", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132750V (27 September 2024); https://doi.org/10.1117/12.3037689

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