Fusion of latent categorical prediction and sequential prediction for session-based recommendation

摘要

Recommendation systems have been becoming ubiquitous in most of online e-commerce platforms, news portals and etc. [1-5], which help users to easily discover their interested items in this era of information explosion. In some application scenarios, users' personal information may not be available beforehand, for example, the click behaviors of a user when browsing websites without logging-in. In such cases, we only have many anonymous users' browsing sequences. Recently, sessionbased recommendation (SBR) has been proposed to predict the next behavior of a user (e.g. the next item to click) for an ongoing anonymous session [6]. According to [7-10], the SBR task can be formally defined as follows. An anonymous session is defined as an item sequence s = { v1, v2, ... , vt} ordered by timestamps, where vi E V represents