CLoSe: Contextualized Location Sequence Recommender

The location-based social networks (LBSN) (e.g., Facebook, etc.) have been explored in the past decade for Point-of-Interest (POI) recommendation. Many of the existing systems focus on recommending a single location or a list which might not be contextually coherent. In this paper, we propose a model termed CLoSe (Contextualized Location Sequence Recommender) that generates contextually coherent POI sequences relevant to user preferences. The POI sequence recommenders are helpful in many day-to-day activities, for e.g., itinerary planning, etc. To the best of our knowledge, this paper is the first to formulate contextual POI sequence recommendation by exploiting Recurrent Neural Network (RNN). We incorporate check-in contexts to the hidden layer and global context to the hidden and output layers of RNN. We also demonstrate the efficiency of extended Long-short term memory (LSTM) in sequence generation. The main contributions of this paper are: (i) it exploits multi-context, personalized user preferences to formulate contextual POI sequence generation, (ii) it presents contextual extensions of RNN and LSTM that incorporate different contexts applicable to a POI and POI sequence, and (iii) it demonstrates significant performance gain of proposed model on pair-F1 and NDCG metrics when evaluated with two real-world datasets.