Paper Info
Title
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.
Year
DOI
Venue
2021
10.1007/978-3-030-85082-1_26
NEW TRENDS IN DATABASE AND INFORMATION SYSTEMS, ADBIS 2021
Keywords
DocType
Volume
Mobility, Diversity, Distance-bound
Conference
1450
ISSN
Citations 
PageRank 
1865-0929
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Xu Teng122.05
Goce Trajcevski21732141.26