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哈工大SCIR一篇长文被WWW 2020任用

国际万维网大会The Web Conference,简称WWW集会)是由国际万维网集会委员会发动主办的国际顶级学术集会,兴办于1994年,每年举办一届,是CCF-A集会。WWW 2020将于2020年4月20日至4月24日中国台湾台北举办。本届集会共收到了1129篇长文投稿,任用217篇长文,任用率为19.2%。

哈尔滨工业大学社会盘算与新闻检索研讨中心有1篇长文被WWW 2020任用,下面是论文简明新闻及摘要:

论文名称:Keywords Generation Improves E-Commerce Session-based Recommendation

作家刘元兴,任昭春,张伟男,车万翔,刘挺,殷大伟

单位哈尔滨工业大学,山东大学,京东

摘要:通过探究细粒度的用户方法,基于会话的引荐应用用户短期内的方法预测用户的下一个举措。昔人的义务仅仅应用了着末一次点击举措举措监视信号。电阛阓景中,因为低容纳性题目(即许众满意用户购物企图的相关产物被引荐系统所疏忽),具有难以捉摸的点击方法和大范围的商品使这个义务具有挑衅性。因为具有差别ID的相似产物可以具有相同的企图,于是我们认为,会话中的文本新闻(比如,商品题目标要害字)可以用作分外的监视信号,以通过进修相似产物中更众的共赞同图来办理上述题目。于是,为了进步基于电商会话的引荐的功用,我们依据目今会话中的点击序次生成要害字来推测用户的企图。

本文中,我们提出了带相要害字生成的基于电商会话的引荐模子(ESRM-KG)。精细地,ESRM-KG模子起首将输入的点击序列编码为高维向量外示;然后应用一种双线性解码,预测目今会话中的下一个举措;同时ESRM-KG模子处理其编码器的高维外示,认为通通会话生成可标明的要害字。我们大范围的电商数据集上举行了大宗的实行。我们的实行结果外明,借帮要害字生成,ESRM-KG模子的功用优于最新的基线。我们还通过样例剖析来议论要害字生成怎样帮帮基于电商会话的引荐。

Abstract:  By exploring fine-grained user behaviors, session-based recommendation predicts a user’s next action from short-term behavior sessions. Most of the previous work learns about a user’s implicit behavior by merely taking the last click action as the supervision signal. However, in e-commerce scenarios, large-scale products with elusive click behaviors make such task challenging because of the low inclusiveness problem, i.e., many relevant products that satisfy the user’s shopping intention are neglected by recommenders. Since similar products with different IDs may share the same intention, we argue that the textual information (e.g., keywords of product titles) from sessions can be used as additional supervision signals to tackle the above problem through learning more shared intention within similar products. Therefore, to improve the performance of e-commerce session-based recommendation, we explicitly infer the user’s intention by generating keywords entirely from the click sequence in the current session.

In this paper, we propose the e-commerce session-based recommendation model with keywords generation (abbreviated as ESRM-KG) to integrate keywords generation into e-commerce session-based recommendation. Specifically, the ESRM-KG model firstly encodes an input action sequence into a high dimensional representation; then it presents a bi-linear decoding scheme to predict the next action in the current session; synchronously, the ESRM-KG model addresses incepts the high dimensional representation of its encoder to generate explainable keywords for the whole session. We carried out extensive experiments in the context of click prediction on a large-scale real-world e-commerce dataset. Our experimental results show that the ESRM-KGmodel outperforms state-of-the-art baselines with the help of keywords generation. We also discuss how keywords generation helps the e-commerce session-based recommendation with case studies and error analysis.

哈工大SCIR
哈工大SCIR

哈尔滨工业大学社会盘算与新闻检索研讨中心

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