Semantically Diverse Constrained Queries

Abstract

Location-Based Services are often used to find proximal Points of Interest (PoI) – e.g., nearby restaurants, museums, etc. – in a plethora of applications. However, one may also desire that the returned proximal objects exhibit (likely) maximal and fine-grained semantic diversity. For instance, rather than picking several close-by attractions with similar features – e.g., restaurants with similar menus; museums with similar art exhibitions – a tourist may be more interested in a result set that could potentially provide more diverse types of experiences, for as long as they are within an acceptable distance from a given (current) location. So far, we introduced a topic modeling approach based on Latent Dirichlet Allocation, a generative statistical model, to effectively model and exploit a fine-grained notion of diversity, based on sets of keywords and/or reviews instead of a coarser user-given category. More importantly, for efficiency, we devised two novel indexing structures – Diversity Map and Diversity Aggregated R-tree. In turn, each of these enabled us to develop efficient algorithms to generate the answer-set for two novel categories of queries. While both queries focus on determining the recommended locations among a set of given PoIs that will maximize the semantic diversity within distance limits along a given road network, they each tackle a different variant. The first type of query is kDRQ, which finds k such PoIs with respect to a given user’s location. The second query kDPQ generates a path to be used to visit a sequence of k such locations (i.e., with max diversity), starting at the user’s current location.