Deep Learning Driven Venue Recommender for Event Based Social Networks
Event based online social platforms, such as Meetup and Plancast, have experienced increased popularity and rapid growth in recent years. In EBSN setup, selecting suitable venues for hosting events, which can attract a great turnout, is a key challenge. In this paper, we present a deep learning based venue recommendation system <inline formula><tex math notation="LaTeX"$DeepVenue$/tex math><alternatives><mml:math><mml:mrow><mml:mi>D</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>V</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>u</mml:mi><mml:mi>e</mml:mi>/mml:mrow>/mml:math><inline graphic xlink:href="pramanik ieq1 2915523.gif"/alternatives>/inline formula> which provides context driven venue recommendations for the <italic>Meetup event hosts</italic> to host their events. The crux of the proposed model relies on the notion of similarity between multiple Meetup entities such as events, venues, groups, etc. We develop deep learning techniques to compute a compact descriptor for each entity, such that two entities (say, venues) can be compared numerically. Notably, to mitigate the scarcity of venue related information in Meetup, we leverage on the cross domain knowledge transfer from popular LBSN service Yelp to extract rich venue related content. For hosting an event, the proposed <inline formula><tex math notation="LaTeX"$DeepVenue$/tex math><alternatives><mml:math><mml:mrow><mml:mi>D</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>V</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>u</mml:mi><mml:mi>e</mml:mi>/mml:mrow>/mml:math><inline graphic xlink:href="pramanik ieq2 2915523.gif"/alternatives>/inline formula> model computes a success score for each candidate venue and ranks those venues according to the scores and finally recommend the top k venues. Our rigorous evaluation on the Meetup data collected for the city of Chicago shows that <inline formula><tex math notation="LaTeX"$DeepVenue$/tex math><alternatives><mml:math><mml:mrow><mml:mi>D</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>V</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>u</mml:mi><mml:mi>e</mml:mi>/mml:mrow>/mml:math><inline graphic xlink:href="pramanik ieq3 2915523.gif"/alternatives>/inline formula> significantly outperforms the baselines algorithms. Precisely, for 84 percent of events, the correct hosting venue appears in the top 5 of the <inline formula><tex math notation="LaTeX"$DeepVenue$/tex math><alternatives><mml:math><mml:mrow><mml:mi>D</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>V</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>u</mml:mi><mml:mi>e</mml:mi>/mml:mrow>/mml:math><inline graphic xlink:href="pramanik ieq4 2915523.gif"/alternatives>/inline formula> recommended list.
IEEE Transactions on Knowledge and Data Engineering