Evaluating User Preferences with a Hybrid Model: Distance-Dependent Chinese Restaurant Process and Weighted Distributions for Content-Based Recommender Systems
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2022
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| _version_ | 1866901360045195264 |
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| author | Dr. Yara Al-Khateeb and Dr. Kaiwen Li |
| author_facet | Dr. Yara Al-Khateeb and Dr. Kaiwen Li |
| contents | <p>—Nowadays websites provide a vast number of resources for users. Recommender systems have been developed as an essential element of these websites to provide a personalized environment for users. They help users to retrieve interested resources from large sets of available resources. Due to the dynamic feature of user preference, constructing an appropriate model to estimate the user preference is the major task of recommender systems. Profile matching and latent factors are two main approaches to identify user preference. In this paper, we employed the latent factor and profile matching to cluster the user profile and identify user preference, respectively. The method uses the Distance Dependent Chines Restaurant Process as a Bayesian nonparametric framework to extract the latent factors from the user profile. These latent factors are mapped to user interests and a weighted distribution is used to identify user preferences. We evaluate the proposed method using a real-world data-set that contains news tweets of a news agency (BBC). The experimental results and comparisons show the superior recommendation accuracy of the proposed approach related to existing methods, and its ability to effectively evolve over time</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19326685 |
| institution | Zenodo |
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| publishDate | 2022 |
| publisher | Zenodo |
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| spellingShingle | Evaluating User Preferences with a Hybrid Model: Distance-Dependent Chinese Restaurant Process and Weighted Distributions for Content-Based Recommender Systems Dr. Yara Al-Khateeb and Dr. Kaiwen Li Content-based recommender systems dynamic user modeling extracting user interests predicting user preference. <p>—Nowadays websites provide a vast number of resources for users. Recommender systems have been developed as an essential element of these websites to provide a personalized environment for users. They help users to retrieve interested resources from large sets of available resources. Due to the dynamic feature of user preference, constructing an appropriate model to estimate the user preference is the major task of recommender systems. Profile matching and latent factors are two main approaches to identify user preference. In this paper, we employed the latent factor and profile matching to cluster the user profile and identify user preference, respectively. The method uses the Distance Dependent Chines Restaurant Process as a Bayesian nonparametric framework to extract the latent factors from the user profile. These latent factors are mapped to user interests and a weighted distribution is used to identify user preferences. We evaluate the proposed method using a real-world data-set that contains news tweets of a news agency (BBC). The experimental results and comparisons show the superior recommendation accuracy of the proposed approach related to existing methods, and its ability to effectively evolve over time</p> |
| title | Evaluating User Preferences with a Hybrid Model: Distance-Dependent Chinese Restaurant Process and Weighted Distributions for Content-Based Recommender Systems |
| topic | Content-based recommender systems dynamic user modeling extracting user interests predicting user preference. |
| url | https://doi.org/10.5281/zenodo.19326685 |