Mean-reversion based hybrid movie recommender system using collaborative and content-based filtering methods

Nasser, Azza Abdel; Bhagat, Jiten; Agrawal, Abhishek; Devadas, T. Joshva

doi:10.22271/maths.2023.v8.i3sb.1012

Cited by 1 publication

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“…The hybrid recommender systems that combine CF and content-based filtering to harness the strengths and mitigate the limitations of both methods have seen increased interest [24][25][26][27]. CF, such as item collaborative filtering (itemCF) and user collaborative filtering (userCF) [28], is effective in modeling user interests, while content-based filtering, like the Deep Attentive Interest Collaborative Filtering (DAICF) model [29], captures rich collaborative signals and user-item interactions. By combining these approaches, hybrid systems can overcome challenges like the cold start issue, data sparsity, and improving recommendation accuracy.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Personalized Recommendations: A Study on the Efficacy of Multi-Task Learning and Feature Integration

Wang,

Jin,

Zhang

et al. 2024

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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, including single-task neural networks, multi-task learning, and baselines, were evaluated with various input feature combinations using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The multi-task learning approach achieved significantly lower RMSE and MAE by effectively leveraging heterogeneous data sources for personalized recommendations through a shared neural network architecture. Furthermore, incorporating user data and content data progressively enhanced performance compared to using only item identifiers. The findings highlight the importance of advanced model architectures and fusing heterogeneous data sources for high-quality recommendations, providing valuable insights for designing effective recommender systems across diverse domains.

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