Research directions in session-based and sequential recommendation

Abstract

Recommender systems are software applications that make tailored item suggestions to users, usually with the goal of helping them overcome information overload or make informed choices. This tailoring process is typically based on the assumption that long-term preference information about the individual users is available to the system, most commonly in the form of a user-item rating matrix. In such a setting, the recommendation problem can be abstracted as a “matrix filling” task (Resnick et al. 1994), and often also the information about the time when the preferences were collected and when the recommendations should be delivered are not considered. In many real-world recommendation scenarios, however, these assumptions might not hold and therefore represent an abstraction of a limited suitability. On the one hand, there can be a substantial number of first-time visitors or anonymous users requesting a recommendation. Clearly, no long-term preference information is avail- able for such users. In this case, providing a tailored recommendation can only be done based on the interactions observed in the ongoing session. On the other hand, when long-term user preference information is available, the temporal dimension of this information may play a role. More recent interactions, for example, might be more relevant than the older ones. In addition, there might also be some order- ing constraints among recommendations, e.g., not recommending a mobile phone.