{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T19:29:27Z","timestamp":1748374167482,"version":"3.38.0"},"reference-count":25,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2024,2,3]]},"abstract":"<jats:p>Sequential recommendation aims to predict users\u2019 future activities based on their historical interaction sequences. Various neural network architectures, such as Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), and self-attention mechanisms, have been employed in the tasks, exploring multiple aspects of user preferences, including general interests, short-term interests, long-term interests, and item co-occurrence patterns. Despite achieving good performance, there are still limitations in capturing complex user preferences. Specifically, the current structures of RNN, GNN, etc., only capture item-level transition relations while neglecting attribute-level transition relations. Additionally, the explicit item relations are studied using item co-occurrence modules, but they cannot capture the implicit item-item relations. To address these issues, we propose a knowledge-augmented Gated Recurrent Unit (GRU) to improve the short-term user interest module and adopt a collaborative item aggregation method to enhance the item co-occurrence module. Additionally, our long-term interest module utilizes a bitwise gating mechanism to select historical item features significant to users\u2019 current preferences. We extensively evaluate our model on three real-world datasets alongside competitive methods, demonstrating its effectiveness in top K sequential recommendation.<\/jats:p>","DOI":"10.3233\/ida-227198","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T16:32:13Z","timestamp":1702398733000},"page":"279-298","source":"Crossref","is-referenced-by-count":1,"title":["Aggregating knowledge and collaborative information for sequential recommendation"],"prefix":"10.1177","volume":"28","author":[{"given":"Yunqi","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing, China"},{"name":"School of Computer and lnformation Technology, Beijing Jiaotong University, Beijing, China"}]},{"given":"Jidong","family":"Yuan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing, China"},{"name":"School of Computer and lnformation Technology, Beijing Jiaotong University, Beijing, China"}]},{"given":"Chixuan","family":"Wei","sequence":"additional","affiliation":[{"name":"Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing, China"},{"name":"School of Computer and lnformation Technology, Beijing Jiaotong University, Beijing, China"}]},{"given":"Yifei","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Engineering, City University of Hong Kong, Hong Kong, China"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-227198_ref1","doi-asserted-by":"crossref","unstructured":"J. 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