会话(web分析)
动力学(音乐)
计算机科学
任务(项目管理)
召回
推荐系统
情报检索
万维网
认知心理学
心理学
教育学
经济
管理
作者
Veronika Bogina,Tsvi Kuflik,Osnat Mokryn
标识
DOI:10.1145/2856767.2856781
摘要
Predicting whether a session is a buying session (e.g. will end with buying an item) is an ongoing research task. Drawing from recent experience in Web search and movie recommenders, we explore the effect of temporal trends and characteristics on the ability to predict buying sessions. We suggest a new approach, based on items' temporal dynamics, together with sessions' temporal aspects for predicting whether a session is going to end up with a purchase. We suggest a model for estimating the probability of a session to end with a purchase, according to the purchase history of items clicked on during the session over the past few days. The predictions can be used by recommender systems, enabling them to take relevant actions, thus improving shoppers experience as well as increasing sales for e-commerce companies. Our findings shed light on the importance of considering temporal dynamics in items recommendations in e-commerce sites. Empirical results on imbalanced e-commerce dataset with more than nine million sessions demonstrate that we achieve high Precision, Recall and ROC in predicting whether session ends up with a purchase or not.
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