Efficient Retrieval of the Top-k Most Relevant Event-Partner Pairs

The proliferation of event-based social networking (EBSN) motivates studies on topics such as event, venue, and friend recommendation as well as event creation and organization. In this setting, the notion of event-partner recommendation has attracted attention. When recommending an event to a user, this functionality allows the recommendation of partners with whom to attend the event. However, in existing proposals, 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 pull and push, 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 top-k k event-partner (k kEP) pair retrieval querying that integrates keyword-based search for events with event-partner recommendation. This type of 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. To compute k 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.