Open AccessAcademic literature recommendation technology based on two-layer attention network
Author(s) -
Hanyang Deng,
Shiping Tang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2024/1/012026
Subject(s) - computer science , limiting , layer (electronics) , base (topology) , artificial intelligence , embedding , macro , deep learning , sequence (biology) , expression (computer science) , machine learning , information retrieval , engineering , mechanical engineering , mathematical analysis , chemistry , mathematics , organic chemistry , biology , genetics , programming language
Recently, academic literature recommendation for learners has become an important topic. Recently deep learning based models are used in literature recommendations, which follow a similar Embedding&MLP paradigm. However, in the base model and its follow-up model Deep Interest Network (DIN), users only have to click or not click on items, limiting the expression of users' specific interests. Therefore, we propose a two-layer attention model (BIH) based on DIN. BIH adds specific behaviors to the user behavior sequence and adds a behavior attention layer, which can learn the expression of user interests more accurately. Experiments on a public dataset and users' behavior logs of academic literature demonstrate the effectiveness of proposed approaches, which achieve superior performance on both Macro-F1 and Micro-F1 compared with the base model and DIN.