Sequences of Recommendations for Dynamic Groups: What Is the Role of Context?

Abstract

Recommendation algorithms have been investigated and employed by many important companies in the past years: some scenarios, such as the one where a system suggests the points of interest to tourists, well adapt to sequence of recommendations to (groups of) users. We envision that sequence recommendations can be useful whenever the group of users has a limited time interval to spend together, since they reduce the time wasted in selecting the best next activity. In this paper, we investigate the role played by the context, i.e. the situation the group is currently experiencing, in the design of a system that recommends sequences of activities. We model the problem as a multi-objective optimization, where the satisfaction of the group and the available time interval are two of the functions to be optimized. In particular, the dynamic evolution of the group can be considered as the key contextual feature to produce better suggestions.