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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n            Due to the easy access, implicit feedback is often used for recommender systems. Compared with point-wise learning and pair-wise learning methods, list-wise rank learning methods have superior performance for top-\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(N\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            recommendation. Recent solutions, especially the list-wise methods, simply treat all interacted items of a user as equally important positives and annotate all no-interaction items of a user as negatives. For the list-wise approaches, we argue that this annotation scheme of implicit feedback is over-simplified due to the sparsity and missing fine-grained labels of the feedback data. To overcome this issue, we revisit the so-called positive and negative samples. First, considering the loss function of list-wise ranking, we analyze the impact of false positives and negatives theoretically. Second, based on the observation, we propose a self-adjusting credibility weight mechanism to re-weigh the positive samples and exploit the higher-order relation based on item\u2013item matrix to sample the critical negative samples. In order to prevent the introduction of noise, we design a pruning strategy for critical negatives. Besides, to combine the reconstruction loss function for the positive samples and critical negative samples, we develop a simple yet effective VAEs framework with linear structure, which abandons the complex non-linear structure. Extensive experiments are conducted on six public real-world datasets. The results demonstrate that, our VAE* outperforms other VAE-based models by a large margin. Besides, we also verify the effect of denoising positives and exploring critical negatives by ablation study.\n          <\/jats:p>","DOI":"10.1145\/3680552","type":"journal-article","created":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T10:09:48Z","timestamp":1723284588000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["VAE*: A Novel Variational Autoencoder via Revisiting Positive and Negative Samples for Top-\n            <i>N<\/i>\n            Recommendation"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8778-7082","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China and University of Macau, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4389-694X","authenticated-orcid":false,"given":"Leong Hou","family":"U","sequence":"additional","affiliation":[{"name":"University of Macau, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1625-2168","authenticated-orcid":false,"given":"Shangsong","family":"Liang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China and Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8263-9032","authenticated-orcid":false,"given":"Huaijie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1340-3995","authenticated-orcid":false,"given":"Jianxing","family":"Yu","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0314-4569","authenticated-orcid":false,"given":"Yubao","family":"Liu","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2834-6252","authenticated-orcid":false,"given":"Jian","family":"Yin","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"273","article-title":"Efficient Top-n Recommendation for Very Large Scale Binary Rated Datasets","author":"Aiolli Fabio","year":"2013","unstructured":"Fabio Aiolli. 2013. 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