One of the most popular applications of Location Based Services (LBS) is recommending a Point of Interest (POI) based on user's preferences and geo-locations. However, the existing approaches have not tackled the problem of jointly determining: (a) a sequence of POIs that can be traversed within certain budget (i.e., limit on distance) and simultaneously provide a high-enough diversity; and (b) recommend the best origin (i.e., the hotel) for a given user, so that the desired route of POIs can be traversed within the specified constraints. In this work, we take a first step towards identifying this new problem and formalizing it as a novel type of a query. Subsequently, we present naïve solutions and experimental observations over a real-life datasets, illustrating the trade-offs in terms of (dis)associating the initial location from the rest of the POIs.
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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.
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- Award ID(s):
- 1823279
- NSF-PAR ID:
- 10211116
- 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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Context has been recognized as an important factor to consider in personalized recommender systems. Particularly in location-based services (LBSs), a fundamental task is to recommend to a mobile user where he/she could be interested to visit next at the right time. Additionally, location-based social networks (LBSNs) allow users to share location-embedded information with friends who often co-occur in the same or nearby points-of-interest (POIs) or share similar POI visiting histories, due to the social homophily theory and Tobler’s first law of geography. So, both the time information and LBSN friendship relations should be utilized for POI recommendation. Tensor completion has recently gained some attention in time-aware recommender systems. The problem decomposes a user-item-time tensor into low-rank embedding matrices of users, items and times using its observed entries, so that the underlying low-rank subspace structure can be tracked to fill the missing entries for time-aware recommendation. However, these tensor completion methods ignore the social-spatial context information available in LBSNs, which is important for POI recommendation since people tend to share their preferences with their friends, and near things are more related than distant things. In this paper, we utilize the side information of social networks and POI locations to enhance the tensor completion model paradigm for more effective time-aware POI recommendation. Specifically, we propose a regularization loss head based on a novel social Hausdorff distance function to optimize the reconstructed tensor. We also quantify the popularity of different POIs with location entropy to prevent very popular POIs from being over-represented hence suppressing the appearance of other more diverse POIs. To address the sensitivity of negative sampling, we train the model on the whole data by treating all unlabeled entries in the observed tensor as negative, and rewriting the loss function in a smart way to reduce the computational cost. Through extensive experiments on real datasets, we demonstrate the superiority of our model over state-of-the-art tensor completion methods.more » « less
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Recommending a Point of Interest (PoI) or a sequence of PoIs to visit based on user’s preferences and geo-locations has been one of the most popular applications of Location-Based Services (LBS). Variants have also been considered which take other factors into consideration, such as broader (implicit or explicit) semantic constraints as well as the limitations on the length of the trip. In this work, we present an efficient algorithmic solution to a novel query –
PaDOC (Paths with Distance, Origin, and Category constraints) – which combines the generation of a path that (a) can be traversed within a user-specified budget (e.g., limit on distance), (b) starts at one of the user-specified origin locations (e.g., a hotel), and (c) contains PoIs from a user-specified list of PoI categories. We show that the problem of deciding whether such a path exists is an NP-hard problem. Based on a novel indexing structure, we propose two efficient algorithms for approximatePaDOC query processing based on both conservative and progressive distance estimations. We conducted extensive experiments over real, publicly available datasets, demonstrating the benefits of the proposed methodologies over straightforward solutions. -
An increasing number of location-based service providers are taking the advantage of cloud computing by outsourcing their Point of Interest (POI) datasets and query services to third-party cloud service providers (CSPs), which answer various location-based queries from users on their behalf. A critical security challenge is to ensure the integrity and completeness of any query result returned by CSPs. As an important type of queries, a location-based skyline query (LBSQ) asks for the POIs not dominated by any other POI with respect to a given query position, i.e., no POI is both closer to the query position and more preferable with respect to a given numeric attribute. While there have been several recent attempts on authenticating outsourced LBSQ, none of them support the shortest path distance that is preferable to the Euclidian distance in metropolitan areas. In this paper, we tackle this open challenge by introducing AuthSkySP, a novel scheme for authenticating outsourced LBSQ under the shortest path distance, which allows the user to verify the integrity and completeness of any LBSQ result returned by an untrusted CSP. We confirm the effectiveness and efficiency of our proposed solution via detailed experimental studies using both real and synthetic datasets.more » « less
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Abstract Aims Mexico harbours a diverse fauna comprising more than 1,400 leafhopper species, 60% of which appear to be strictly endemic, with many apparently restricted to particular habitats and host plants. The aims of this study were to identify areas of high species richness and endemism, and determine the biogeographic affinities of species in the diverse arboreal tribe Athysanini to provide data useful for conservation prioritization.
Location Mexico.
Methods A data set of 643 historical occurrence records based on authoritatively identified specimens from museums, recent fieldwork, literature and digital repositories was analysed. Analyses of species richness and areas of endemism were conducted using geographic information systems.
Results A total of 164 species belonging of 50 genera were included, of which 145 species of 31 genera are considered to be endemic. The Mexican state of Guerrero yielded the most species records (48%). The highest numbers of taxa and endemic species were concentrated along the Mexican Transition Zone (MTZ) within which the Trans‐Mexican Volcanic Belt (TVB) province had the most species records. Endemicity analyses showed two different geographical patterns but similar species richness weights with overlapping values over the MTZ. Distribution across vegetation types was not uniform, with most athysanine species concentrated in the dry tropical forest (65%). Species were documented at elevations between sea level and 3,200 m a.s.l. with three altitudinal preference classes. Conservation assessments applying IUCN criteria categorized a majority of species (145) as endangered or critically endangered.
Main conclusions Our findings suggest that most identified areas of high species richness throughout the territory have predominantly endemic taxa. Distributional patterns found are non‐random, influenced by richness and endemism values over the TVB province and in the MTZ with a variable dispersion among species. Data highlight a greatly threatened status by habitat loss, remarking an urgent need for an improved conservation framework.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MDM48529.2020.00028
