An Efficient Indexing for Top-k Query Answering in Location-Based Recommendation System

Location-based recommendation systems are obtaining interests from both the business and research communities recently. In this paper, we propose an indexing approach to improve the efficiency of the top-k query answering for a prominent location-based recommendation model, User-centered collaborative location and activity filtering (UCLAF). The efficiency issue of such query type is important since there could be enormous users, locations, and activities in the recommendation model. When a query is issued, not all of the answers are to be obtained, but only a few most-relevant answers. Our proposed work is based on a multi-dimensional index, aR-Tree. A feature of such index tree, i.e. only-relevant information traversal, is utilized with some modification. In addition, the experiments have been conducted to evaluate our proposed work. In which, the results, which our work is compared with a few indexing methods, show that our work is highly efficient when system is scaled up.