In Good Company: Efficient Retrieval of the Top-k Most Relevant Event-Partner Pairs

The proliferation of event-based social networking (ESBN) motivates a range of studies on topics such as event, venue, and friend recommendation and event creation and organization. In this setting, the notion of event-partner recommendation has recently attracted attention. When recommending an event to a user, this functionality allows recommendation of partner with whom to attend the event. However, existing proposals are push-based: recommendations are pushed to users at the system’s initiative. In contrast, EBSNs provide users with keyword-based search functionality. This way, users may retrieve information in pull mode. We propose a new way of accessing information in EBSNs that combines push and pull, thus allowing users to not only conduct ad-hoc searches for events, but also to receive partner recommendations for retrieved events. Specifically, we define and study the top-k event-partner (kEP) pair retrieval query that integrates event-partner recommendation and keyword-based search for events. The query retrieves event-partner pairs, taking into account the relevance of events to user-supplied keywords and so-called together preferences that indicate the extent of a user’s preference to attend an event with a given partner. In order to compute kEP queries efficiently, we propose a rank-join based framework with three optimizations. Results of empirical studies with implementations of the proposed techniques demonstrate that the proposed techniques are capable of excellent performance.