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Dongre Deepak Mahapatrav

Bio: Dongre Deepak Mahapatrav is an academic researcher. The author has contributed to research in topics: Ranking & Recommender system. The author has an hindex of 1, co-authored 1 publications receiving 42 citations.

Papers
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Journal Article
TL;DR: Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.
Abstract: The problem of hyper-local place ranking. Given a user location and query string (e.g., “Indian restaurant"), hyper-local ranking provides a list of top-k points of interest influenced by previously logged directional queries (e.g., map direction searches from point A to point B).This paper proposes LARS*, a location-aware recommender system that uses their location-based ratings to show recommendations. Traditional recommender systems do not have spatial properties of users nor items; LARS*, next, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS* exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS* can apply these techniques separately, or together, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches. Our proposed location-aware recommender system, tackles a problem untouched by traditional recommender systems by dealing with three types of location-based ratings: spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* employs user partitioning and travel penalty techniques to support spatial ratings and spatial items, respectively.

42 citations


Cited by
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Journal ArticleDOI
01 Jun 2017
TL;DR: An all-around evaluation of 12 state-of-the-art POI recommendation models to provide readers with an overall picture of the cutting-edge research onPOI recommendation and obtain several important findings.
Abstract: Point-of-interest (POI) recommendation is an important service to Location-Based Social Networks (LBSNs) that can benefit both users and businesses. In recent years, a number of POI recommender systems have been proposed, but there is still a lack of systematical comparison thereof. In this paper, we provide an all-around evaluation of 12 state-of-the-art POI recommendation models. From the evaluation, we obtain several important findings, based on which we can better understand and utilize POI recommendation models in various scenarios. We anticipate this work to provide readers with an overall picture of the cutting-edge research on POI recommendation.

257 citations

Journal ArticleDOI
TL;DR: To review some of the most important contributions in this domain to understand the principles of SIR, a taxonomy to categorize these contributions, and an analysis of some of these contributions and tools with respect to several criteria are proposed.

92 citations

Proceedings ArticleDOI
16 Apr 2018
TL;DR: The experimental studies conducted on both real and synthetic datasets demonstrate that the proposed rotating-circle-based algorithms can compute all RB-k-cores very efficiently and significantly outperforms the existing techniques for computing the minimum circle-bounded k-core.
Abstract: Driven by real-life applications in geo-social networks, in this paper, we investigate the problem of computing the radius-bounded k-cores (RB-k-cores) that aims to find cohesive subgraphs satisfying both social and spatial constraints on large geo-social networks. In particular, we use k-core to ensure the social cohesiveness and we use a radius-bounded circle to restrict the locations of users in a RB-k-core. We explore several algorithmic paradigms to compute RB-k-cores, including a triple vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating circle-based paradigm is further enhanced with several pruning techniques to achieve better efficiency. The experimental studies conducted on both real and synthetic datasets demonstrate that our proposed rotating-circle-based algorithms can compute all RB-k-cores very efficiently. Moreover, it can also be used to compute the minimum-circle-bounded k-core and significantly outperforms the existing techniques for computing the minimum circle-bounded k-core.

83 citations

Proceedings ArticleDOI
03 May 2016
TL;DR: The difficulties faced by indoor positioning systems are reviewed, the requirement for a large signal bandwidth is reviewed and how a lack of bandwidth can be compensated by multi-antenna systems.
Abstract: Highly accurate and reliable indoor positioning — at accuracy levels in the 10 cm range — will enable a large a number of innovative location-based applications because such accuracy levels essentially allow for a useful real-time interaction of humans and cyber-physical systems. Activity recognition, navigation at "shelf" level, geofencing, process monitoring and process control are among the envisioned services that will yield numerous applications in various domains. This paper reviews the difficulties faced by indoor positioning systems, motivating the requirement for a large signal bandwidth and how a lack of bandwidth can be compensated by multi-antenna systems. The potential capabilities of upcoming generations of wireless systems will increasingly make high-accuracy positioning available in near future.

61 citations

Journal ArticleDOI
TL;DR: This work analyzed users’ check-in behavior in detail and developed a deep learning model to integrate geographical and social influences for POI recommendation tasks, using a semi-restricted Boltzmann machine and a conditional layer to model the social influence.
Abstract: Personalized point-of-interest (POI) recommendation is important to location-based social networks (LBSNs) for helping users to explore new places and for helping third-party services to launch tar...

57 citations