HiCaPS: hierarchical contextual POI sequence recommender

The Point-of-Interest (POI) preference of a user varies by locality, item type, and the co-visitors, e.g., user₁ and user₂ can have closest preference on food items but not on historic sites, etc. A locality can have different preference trends (e.g., popular for food, recreation, etc.) and a user's preference can span across multiple such trends. A good recommender should also exploit the aggregated locality preference trends. Most of the existing studies group items by category or global user preferences which might not be relevant for locality-based aggregated preferences. We propose HiCaPS (<u>Hi</u>erarchical <u>C</u>ontextual <u>P</u>OI <u>S</u>equence Recommender) that formulates user preferences as hierarchical structure and presents a hierarchy aggregation technique for POI recommendation. The top level of locality hierarchy contains preferred k items from a set of users and the subsequent levels contain preference wise subsets. The core contributions of this paper are: (i) it formulates user preferences as a preference hierarchy, presents a technique to aggregate preference hierarchies of a similar users, and models the target users' preference in terms of aggregated trend in a locality, (ii) it contextually exploits the aggregated trend to generate personalized POI sequences, and (iii) it extensively evaluates the proposed model with two real-world datasets and demonstrates performance gain (0.03 - 0.28 on pair F-score, 0.006 - 5.91 on diversity, 0.0349 - 17.51 on displacement, and 0.114 - 0.289 on NDCG) over baseline models.