Sequence recommendations for groups: A dynamic approach to balance preferences

We are living in the age of recommendations: it has been estimated that two-thirds of the films viewed on Netflix come from recommendations while the 35% of Amazon sales regard goods suggested to users. There are many factors to consider when providing a new suggestion: in addition to being useful, it should also be relevant and serendipitous, starting from historical data previously collected. In particular, the notion of context has to be considered since it induces some dynamic aspects in the definition of user preferences. The role of context becomes particularly important when we shift from single (myopic) suggestions to be provided to an individual user, to sequences of recommendations for groups of users. When the preferences of individual users are combined to define the preference of a new ephemeral group, dynamic contextual concerns have to be considered in order to provide the best possible experience and extend the group life, preventing the defection of some members because their preferences are not balanced. In this paper we introduce our proposal for producing sequences of recommendations for groups of users which is based on the Multi-Objective Simulated Annealing optimization technique and takes into account dynamic aspects. Moreover, we propose some strategies for extracting the required dynamic information from log data typically available and present the experimental results of the application of our approach in some real-world case studies.