PNS4Rec:利用Personalized Next Session改善基於Session的推薦系統;PNS4Rec: Personalized Next Session for Session-based Recommendation

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > Electronic Thesis & Dissertation > Item 987654321/89788

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/89788

Title:	PNS4Rec:利用Personalized Next Session改善基於Session的推薦系統;PNS4Rec: Personalized Next Session for Session-based Recommendation
Authors:	施柏丞;Shih, Po-Cheng
Contributors:	資訊管理學系
Keywords:	基於Session的推薦;深度學習;個性化推薦;神經網路;全局資訊;Session 嵌入;Session-based recommendation;Deep learning;Personalized recommendation;Neural network;Global information;Session embedding
Date:	2022-07-11
Issue Date:	2022-10-04 11:59:46 (UTC+8)
Publisher:	國立中央大學
Abstract:	在電子商務中，由於Session中有關用戶的資訊有限，要提供基於Session的個人化推薦仍然是一項具有挑戰性的工作。因此，本文認為僅僅基於用戶個人的局部資訊（使用者個人行為），即只包含個人的長期資訊以及短期資訊是遠遠不夠的，應該納入全局資訊作為考慮，進而從現有的Session中，挖掘出更多可利用的協作訊息。然而我們並非參考所有的全局資訊，而是從中挑選對用戶有用的Session，即所謂的Personalized Next Session (PNS)做為全局資訊，以改善局部資訊的不足。本文是第一個採用包含PNS概念的深度網路架構研究，並同時結合局部資訊與全局資訊，以向使用者推薦當前Session的下一個可能交互項目。我們對多個真實世界的資料集進行了實驗，大量的實驗結果表明我們提出的深度網路架構性能優於幾種最先進的推薦方法。 ;In e-commerce, personalized session-based recommendation is still a challenging task because of the limited information about the user in the session. Therefore, in this study, we believe that it is far from enough to include only the user′s local information (user′s personal behavior), i.e., only personal long-term information and short-term information, and we should take global information into consideration to explore more collaborative information from existing sessions. However, we do not refer to all the global information, but select sessions that are useful to users, the so-called Personalized Next Session (PNS) as the global information, in order to improve the shortage of local information. This work is the first to adopt a deep network architecture research that includes the concept of PNS, and combines local and global information at the same time to recommend the next item of the current session to the user. We have conducted experiments on several real-world datasets. Extensive experimental results show that our proposed deep network architecture outperforms several of the most advanced recommendation methods.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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