Comparison Between Collaborative Filtering and Content-Based Filtering

Guo, Shirong

doi:10.54097/hset.v16i.2627

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Collaborative filtering (CF) and content-based filtering continue to serve as a foundation for recommender systems [12][13][14][15]. CF methods, such as matrix completion, remain prevalent due to their effectiveness in capturing user-item interactions [16,17].…”

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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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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A Hybrid Approach for Mobile Phone Recommendation using Content-Based and Collaborative Filtering

Chandrahaas,

Panigrahi,

Pande

et al. 2023

EAI Endorsed Trans IoT

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INTRODUCTION: The number of manufacturers and models accessible in the market has increased due to the growing trend of mobile phone use. Customers now have the difficult task of selecting a phone that both fits their demands and offers good value. Although recommendation algorithms already exist, they frequently overlook the various aspects that buyers take into account before making a phone purchase. Furthermore, recommendation systems are now widely used tools for using huge data and customising suggestions according to user preferences. OBJECTIVES: Machine learning techniques like content-based filtering and collaborative filtering have demonstrated promising outcomes among the different methodologies proposed for constructing these kinds of systems. A hybrid recommendation system that combines the benefits of collaborative filtering with content-based filtering is presented in this paper for mobile phone choosing. The suggested method intends to deliver more precise and customised recommendations by utilising user behaviour patterns and mobile phone content properties. METHODS: The system makes better recommendations by analysing user preferences and phone similarities using the aforementioned machine learning methods. The technology that has been built exhibits its capability to aid customers in selecting a mobile phone with knowledge. RESULTS: With the effective Hybridization process we have obtained the best possible scores of MSE, MAE and RMSE. CONCLUSION: To sum up, the growing intricacy of the mobile phone industry and the abundance of options have demanded the creation of increasingly advanced recommendation systems. This work presents a hybrid recommendation system that efficiently blends collaborative and content-based filtering techniques to provide users with more tailored, superior recommendations. This approach has the ability to enable customers to choose the best mobile phone for their needs by taking into account both user behaviour and mobile phone characteristics.

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Comparison Between Collaborative Filtering and Content-Based Filtering

Cited by 2 publications

References 6 publications

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

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

A Hybrid Approach for Mobile Phone Recommendation using Content-Based and Collaborative Filtering

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