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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2207.12515 |
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| _version_ | 1866916133626445824 |
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| author | Ge, Yingqiang Liu, Shuchang Fu, Zuohui Tan, Juntao Li, Zelong Xu, Shuyuan Li, Yunqi Xian, Yikun Zhang, Yongfeng |
| author_facet | Ge, Yingqiang Liu, Shuchang Fu, Zuohui Tan, Juntao Li, Zelong Xu, Shuyuan Li, Yunqi Xian, Yikun Zhang, Yongfeng |
| contents | Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation, robustness in recommendation, user-controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_12515 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Survey on Trustworthy Recommender Systems Ge, Yingqiang Liu, Shuchang Fu, Zuohui Tan, Juntao Li, Zelong Xu, Shuyuan Li, Yunqi Xian, Yikun Zhang, Yongfeng Information Retrieval Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation, robustness in recommendation, user-controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation. |
| title | A Survey on Trustworthy Recommender Systems |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2207.12515 |