Jian Feng*, Yuwen Wang, Shaojian Chen
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 397-413, 2023, DOI:10.32604/cmc.2023.038741
Abstract Session-based Recommendation (SBR) aims to accurately recommend a list of items to users based on anonymous historical session sequences. Existing methods for SBR suffer from several limitations: SBR based on Graph Neural Network often has information loss when constructing session graphs; Inadequate consideration is given to influencing factors, such as item price, and users’ dynamic interest evolution is not taken into account. A new session recommendation model called Price-aware Session-based Recommendation (PASBR) is proposed to address these limitations. PASBR constructs session graphs by information lossless approaches to fully encode the original session information, then introduces item price as a new… More >
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