Enhancing Personalized Recommendations: A Study on the Efficacy of Multi-Task Learning and Feature Integration
Qinyong Wang,
Enman Jin,
Huizhong Zhang
et al.
Abstract:Personalized recommender systems play a crucial role in assisting users in discovering items of interest from vast amounts of information across various domains. However, developing accurate personalized recommender systems remains challenging due to the need to balance model architectures, input feature combinations, and fusion of heterogeneous data sources. This study investigates the impacts of these factors on recommendation performance using the MovieLens and Book Recommendation datasets. Six models, incl… Show more
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