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Technol."],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>Heterogeneous sequential recommendation that models sequences of items associated with more than one type of feedback such as examinations and purchases is an emerging topic in the research community, which is also an important problem in many real-world applications. Though there are some methods proposed to exploit different types of feedback in item sequences such as RLBL, RIB, and BINN, they are based on RNN and may not be very competitive in capturing users\u2019 complex and dynamic preferences. And most existing advanced sequential recommendation methods such as the CNN- and attention-based methods are often designed for making use of item sequences with one single type of feedback, which thus can not be applied to the studied problem directly. As a response, we propose a novel feedback-aware local and global (FLAG) preference learning model for heterogeneous sequential recommendation. Our FLAG contains four modules, including (i) a local preference learning module for capturing a user\u2019s short-term interest, which adopts a novel feedback-aware self-attention block to distinguish different types of feedback; (ii) a global preference learning module for modeling a user\u2019s global preference; (iii) a local intention learning module, which takes a user\u2019s real feedback in the next step, i.e., the user\u2019s intention at the current step, as the query vector in a self-attention block to figure out the items that match the user\u2019s intention well; and (iv) a prediction module for preference integration and final prediction. We then conduct extensive experiments on three public datasets and find that our FLAG significantly outperforms 13 very competitive baselines in terms of two commonly used ranking-oriented metrics in most cases. We also include ablation studies and sensitivity analysis of our FLAG to have more in-depth insights.<\/jats:p>","DOI":"10.1145\/3557046","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T12:35:15Z","timestamp":1660653315000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["FLAG: A Feedback-aware Local and Global Model for Heterogeneous Sequential Recommendation"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3017-9596","authenticated-orcid":false,"given":"Mingkai","family":"He","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6472-9982","authenticated-orcid":false,"given":"Jing","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1415-5826","authenticated-orcid":false,"given":"Jinwei","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6326-9531","authenticated-orcid":false,"given":"Weike","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6933-5760","authenticated-orcid":false,"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462968"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5329"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3326937.3341261"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00035"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3402521"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304232"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2958808"},{"key":"e_1_3_2_9_1","first-page":"249","volume-title":"Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (JMLR Proceedings)","volume":"9","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. 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