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Conventional sequential recommendation models typically express each item with a uniform embedding, ignoring evolutionary patterns among item attributes, such as category, brand, and price. Moreover, these models often model users\u2019 long- and short-term interests independently, failing to adequately address the issues of interest drift and short-term interest evolution. This study proposes a new model, the Feature-aware Long-Short Interest Evolution Network (FLSIE), to address the above-mentioned issues. Specifically, the model uses explicit feature embedding to represent item attribute information and employs a two-dimensional (2D) attention mechanism to distinguish the significance of individual features in a specific item and the relevance of each item in the interaction sequence. Furthermore, to avoid the issue of interest drift, the model employs a long-term interest guidance mechanism to enhance the representation of short-term interest and adopts a gated recurrent unit with attentional update gate to model the dynamic evolution of users\u2019 short-term interest. Experimental results indicate that our presented model outperforms existing methods on three real-world datasets.<\/jats:p>","DOI":"10.3233\/ida-230288","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T15:13:29Z","timestamp":1694790809000},"page":"733-750","source":"Crossref","is-referenced-by-count":1,"title":["A feature-aware long-short interest evolution network for sequential recommendation"],"prefix":"10.1177","volume":"28","author":[{"given":"Jing","family":"Tang","sequence":"first","affiliation":[]},{"given":"Yongquan","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Yajun","family":"Du","sequence":"additional","affiliation":[]},{"given":"Xianyong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaoliang","family":"Chen","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/IDA-230288_ref1","doi-asserted-by":"crossref","unstructured":"R. Ma, N. Liu, J. Yuan, H. 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