Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913447938097152 |
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| author | Wu, Haolun Meshi, Ofer Zoghi, Masrour Diaz, Fernando Liu, Xue Boutilier, Craig Karimzadehgan, Maryam |
| author_facet | Wu, Haolun Meshi, Ofer Zoghi, Masrour Diaz, Fernando Liu, Xue Boutilier, Craig Karimzadehgan, Maryam |
| contents | Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t.\ accuracy, diversity, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel method that leverages Gaussian process regression (GPR) for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_20091 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval Wu, Haolun Meshi, Ofer Zoghi, Masrour Diaz, Fernando Liu, Xue Boutilier, Craig Karimzadehgan, Maryam Information Retrieval Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t.\ accuracy, diversity, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel method that leverages Gaussian process regression (GPR) for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty. |
| title | Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2310.20091 |