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Inf. Syst."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>\n            Preference logical reasoning utilizes user-item interactions (e.g., ratings and reviews) to infer user preferences and discover user decision paths from the knowledge graph to enhance the explainability of item recommendations. However, existing algorithms assume that the ratings and reviews of any item are always consistent, ignoring situations where items with high ratings have negative reviews or items with low ratings but positive reviews. This leads to inaccurate learning of user preferences. In fact, through experimental analysis of two real datasets, we found that on average, about 10% of the interactive data exhibited this inconsistency, that is, items with high ratings but negative reviews appear in the recommendation list. To address this issue, we propose a general preference logical reasoning method based on preference operators. Specifically, we capture the semantic information of users toward the item (its corresponding attributes) in reviews and define two preference operators (\n            <jats:italic toggle=\"yes\">like<\/jats:italic>\n            and\n            <jats:italic toggle=\"yes\">dislike<\/jats:italic>\n            ) for the item to correct ambiguous neutral ratings or false ratings that do not reflect true preferences. In the process of preference path reasoning, the\n            <jats:italic toggle=\"yes\">like<\/jats:italic>\n            preference operator increases the occurrence probability of liked items, while the\n            <jats:italic toggle=\"yes\">dislike<\/jats:italic>\n            preference operator reduces the occurrence probability of disliked items. By fusing the preference operators in the preference path, we obtain consistent user preferences and enhance the explainability of item recommendations. The experimental results on four real datasets demonstrate that our method can effectively improve the performance of all comparison baselines in terms of recommendation accuracy and user decision explainability.\n          <\/jats:p>","DOI":"10.1145\/3733596","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T10:54:17Z","timestamp":1746183257000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Preference Logical Reasoning with Preference Operators for Explainable Recommendations"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1372-3999","authenticated-orcid":false,"given":"Fei","family":"Li","sequence":"first","affiliation":[{"name":"Software College, Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5419-5286","authenticated-orcid":false,"given":"Enneng","family":"Yang","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1709-5056","authenticated-orcid":false,"given":"Guibing","family":"Guo","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7492-0473","authenticated-orcid":false,"given":"Linying","family":"Jiang","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4492-5075","authenticated-orcid":false,"given":"Jianzhe","family":"Zhao","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2856-4716","authenticated-orcid":false,"given":"Xingwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China"}]}],"member":"320","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/a11090137"},{"key":"e_1_3_3_3_2","first-page":"2787","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Dur\u00e1n, Jason Weston, and Oksana Yakhnenko. 2013. 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