Name
Abstract | ||
|---|---|---|
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.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3274895.3274925 | SIGSPATIAL/GIS |
Keywords | Field | DocType |
Hierarchical recommender, Social Networks, POI Recommender | Learning to rank,Locality,Social network,Information retrieval,Computer science,Recreation,Exploit,Artificial intelligence,Hierarchy,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5889-7 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramesh Baral | 1 | 22 | 2.47 |
| S. S. Iyengar | 2 | 152 | 25.22 |
| Tao Li | 3 | 7216 | 393.45 |
| Xiaolong Zhu | 4 | 298 | 28.11 |