Speaker: Xu Teng, ECpE Graduate Student 
Advisor: Goce Trajcevski
Title: Semantically diverse and spatially constrained queries
Abstract: One of the most popular applications of Location-Based Services (LBS) is to recommend proximal Points of Interest (PoI) – e.g., nearby restaurants, museums, police stations, hospitals, etc. – or a sequence of PoIs to visit. An important recently addressed variant of the problem not only considers the preference of distance/proximity, but also desires that the returned proximal objects satisfy certain semantic constraints. 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, as long as they are within an acceptable distance from a given (current) location. In our research so far, we introduced a topic modeling approach based on the Latent Dirichlet Allocation (LDA), a generative statistical model, to effectively model and exploit a fine-grained notion of diversity, namely based on sets of keywords and/or reviews instead of a coarser user-given category. More importantly, for efficiency purpose, 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 are focusing 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. Our experimental evaluations conducted on real-world datasets demonstrate the benefits of each of the proposed methodologies over different baseline approaches.
Biography: Xu Teng received the M.S. degree in Computer Science from Northwestern University, in 2016. He is currently a Ph.D. candidate in Computer Engineering at Iowa State University. His primary research has been focused on Geographic Information System and spatial indexing structure with applications in location-based services and path planning queries.