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Title: Semantically Diverse Path Search
Location-Based Services are often used to find proximal Points of Interest PoI - e.g., nearby restaurants and museums, police stations, hospitals, etc. - in a plethora of applications. An important recently addressed variant of the problem not only considers the distance/proximity aspect, but also desires semantically diverse locations in the answer-set. 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. Towards that goal, in this work we propose a novel approach to efficiently retrieve a path that will maximize the semantic diversity of the visited PoIs that are within distance limits along a given road network. We introduce a novel indexing structure - the Diversity Aggregated R-tree, based on which we devise efficient algorithms to generate the answer-set - i.e., the recommended locations among a set of given PoIs - relying on a greedy search strategy. Our experimental evaluations conducted on real datasets demonstrate the benefits of proposed methodology over the baseline alternative approaches.  more » « less
Award ID(s):
1823279
NSF-PAR ID:
10211116
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
21st {IEEE} International Conference on Mobile Data Management, {MDM} 2020, Versailles, France, June 30 - July 3, 2020
Page Range / eLocation ID:
69 to 78
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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