scholarly journals Querying Optimal Routes for Group Meetup

2021 ◽  
Author(s):  
Bo Chen ◽  
Huaijie Zhu ◽  
Wei Liu ◽  
Jian Yin ◽  
Wang-Chien Lee ◽  
...  
Keyword(s):  
Lower Bound ◽  
Search Space ◽  
Approximation Ratio ◽  
Location Based Services ◽  
Approximate Algorithm ◽  
Shopping Malls ◽  
Gas Stations ◽  
Point Of Interest ◽  
Novel Variant ◽  
Location Based Social Networks

AbstractMotivated by location-based social networks which allow people to access location-based services as a group, we study a novel variant ofoptimal sequenced route(OSR) queries, optimal sequenced route for group meetup (OSR-G) queries. OSR-G query aims to find the optimal meeting POI (point of interest) such that the maximum users’ route distance to the meeting POI is minimized after each user visits a number of POIs of specific categories (e.g., gas stations, restaurants, and shopping malls) in a particular order. To process OSR-G queries, we first propose anOSR-Based(OSRB) algorithm as our baseline, which examines every POI in the meeting category and utilizes existing OSR (calledE-OSR) algorithm to compute the optimal route for each user to the meeting POI. To address the shortcomings (i.e., requiring to examine every POI in the meeting category) ofOSRB, we propose anupper bound based filteringalgorithm, calledcircle filtering(CF) algorithm, which exploits the circle property to filter the unpromising meeting POIs. In addition, we propose alower bound based pruning(LBP) algorithm, namelyLBP-SPwhich exploits a shortest path lower bound to prune the unqualified meeting POIs to reduce the search space. Furthermore, we develop an approximate algorithm, namely APS, to accelerate OSR-G queries with a good approximation ratio. Finally the experimental results show that bothCFandLBP-SPoutperform theOSRBalgorithm and have high pruning rates. Moreover, the proposed approximate algorithm runs faster than the exact OSR-G algorithms and has a good approximation ratio.

scholarly journals User Modeling for Point-of-Interest Recommendations in Location-Based Social Networks: The State of the Art

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Shudong Liu
Keyword(s):  
Social Networks ◽  
User Modeling ◽  
Real Life ◽  
Location Based Services ◽  
Geographical Information ◽  
Daily Movement ◽  
Spatiotemporal Information ◽  
Point Of Interest ◽  
Future Challenges ◽  
Location Based Social Networks

The rapid growth of location-based services (LBSs) has greatly enriched people’s urban lives and attracted millions of users in recent years. Location-based social networks (LBSNs) allow users to check-in at a physical location and share daily tips on points of interest (POIs) with their friends anytime and anywhere. Such a check-in behavior can make daily real-life experiences spread quickly through the Internet. Moreover, such check-in data in LBSNs can be fully exploited to understand the basic laws of humans’ daily movement and mobility. This paper focuses on reviewing the taxonomy of user modeling for POI recommendations through the data analysis of LBSNs. First, we briefly introduce the structure and data characteristics of LBSNs, and then we present a formalization of user modeling for POI recommendations in LBSNs. Depending on which type of LBSNs data was fully utilized in user modeling approaches for POI recommendations, we divide user modeling algorithms into four categories: pure check-in data-based user modeling, geographical information-based user modeling, spatiotemporal information-based user modeling, and geosocial information-based user modeling. Finally, summarizing the existing works, we point out the future challenges and new directions in five possible aspects.

Design and Implementation of a Location-Based Service With Emphasis on a Geographical Database

2021 ◽  
pp. 1384-1397
Author(s):  
Wen-Chen Hu
Keyword(s):  
Data Storage ◽  
Location Based Services ◽  
Security And Privacy ◽  
Simple Task ◽  
Server Side ◽  
Gas Stations ◽  
Design And Implementation ◽  
Construction Step ◽  
Geographic Data ◽  
Location Based Social Networks

One of the most popular apps is location-based services (LBSs) such as navigation, location-based social networks, and location-based advertisements. However, building an LBS is not a simple task because it involves various subjects and techniques like mobile computing, databases, and security and privacy. One of the major LBS components is geographical databases, which are used to store geographic data like locations and functions (such as restaurants and gas stations). A geographical database is usually hosted on a server because of its huge size and should facilitate geographic data storage, indexing, searching, and matching. This article tries to mitigate the high difficulty of LBS construction by showing the construction step by step with a focus on connecting a mobile device to a server-side geographical database. After reading this article, readers will be able to build an LBS prototype for their research or applications.

scholarly journals Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective

2022 ◽  
Author(s):  
Pablo Sánchez ◽  
Alejandro Bellogín
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Information Sources ◽  
Learning Approaches ◽  
Performance Improvements ◽  
Point Of Interest ◽  
Real Performance ◽  
Research Questions ◽  
Developing Area ◽  
Location Based Social Networks

Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.

scholarly journals Deterministic Dynamic Matching in O(1) Update Time

Algorithmica ◽  
2019 ◽  
Vol 82 (4) ◽  
pp. 1057-1080 ◽  
Author(s):  
Sayan Bhattacharya ◽  
Deeparnab Chakrabarty ◽  
Monika Henzinger
Keyword(s):  
Computer Science ◽  
Vertex Cover ◽  
Approximation Ratio ◽  
Approximate Algorithm ◽  
Maximum Matching ◽  
Matching Problem ◽  
Dynamic Algorithm ◽  
Dynamic Matching ◽  
Deterministic Dynamic ◽  
Oblivious Adversary

Abstract We consider the problems of maintaining an approximate maximum matching and an approximate minimum vertex cover in a dynamic graph undergoing a sequence of edge insertions/deletions. Starting with the seminal work of Onak and Rubinfeld (in: Proceedings of the ACM symposium on theory of computing (STOC), 2010), this problem has received significant attention in recent years. Very recently, extending the framework of Baswana et al. (in: Proceedings of the IEEE symposium on foundations of computer science (FOCS), 2011) , Solomon (in: Proceedings of the IEEE symposium on foundations of computer science (FOCS), 2016) gave a randomized dynamic algorithm for this problem that has an approximation ratio of 2 and an amortized update time of O(1) with high probability. This algorithm requires the assumption of an oblivious adversary, meaning that the future sequence of edge insertions/deletions in the graph cannot depend in any way on the algorithm’s past output. A natural way to remove the assumption on oblivious adversary is to give a deterministic dynamic algorithm for the same problem in O(1) update time. In this paper, we resolve this question. We present a new deterministic fully dynamic algorithm that maintains a O(1)-approximate minimum vertex cover and maximum fractional matching, with an amortized update time of O(1). Previously, the best deterministic algorithm for this problem was due to Bhattacharya et al. (in: Proceedings of the ACM-SIAM symposium on discrete algorithms (SODA), 2015); it had an approximation ratio of $$(2+\varepsilon )$$(2+ε) and an amortized update time of $$O(\log n/\varepsilon ^2)$$O(logn/ε2). Our result can be generalized to give a fully dynamic $$O(f^3)$$O(f3)-approximate algorithm with $$O(f^2)$$O(f2) amortized update time for the hypergraph vertex cover and fractional hypergraph matching problem, where every hyperedge has at most f vertices.

scholarly journals Dynamic Recommendation of POI Sequence Responding to Historical Trajectory

2019 ◽  
Vol 8 (10) ◽  
pp. 433 ◽  
Author(s):  
Jianfeng Huang ◽  
Yuefeng Liu ◽  
Yue Chen ◽  
Chen Jia
Keyword(s):  
Deep Neural Network ◽  
Rapid Development ◽  
Input Sequence ◽  
Learning Task ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Recommendation Accuracy ◽  
Location Based Social Networks ◽  
Historical Trajectory ◽  
Over Time

Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the output sequence is exactly the POI sequence to be recommended. To solve this Seq2Seq problem, an effective architecture is designed based on the Deep Neural Network (DNN). Owing to the end-to-end workflow, DRPS can easily make dynamic POI sequence recommendations by allowing the input to change over time. In addition, two new metrics named Aligned Precision (AP) and Order-aware Sequence Precision (OSP) are proposed to evaluate the recommendation accuracy of a POI sequence, which considers not only the POI identity but also the visiting order. The experimental results show that the proposed method is effective for POI sequence recommendation tasks, and it significantly outperforms the baseline approaches like Additive Markov Chain, LORE and LSTM-Seq2Seq.

TWO FOR ONE: TIGHT APPROXIMATION OF 2D BIN PACKING

2013 ◽  
Vol 24 (08) ◽  
pp. 1299-1327 ◽  
Author(s):  
ROLF HARREN ◽  
KLAUS JANSEN ◽  
LARS PRÄDEL ◽  
ULRICH M. SCHWARZ ◽  
ROB VAN STEE
Keyword(s):  
Lower Bound ◽  
Knapsack Problem ◽  
Bin Packing ◽  
Packing Problem ◽  
Approximate Algorithm ◽  
Two Dimensional ◽  
Chip Design ◽  
Practical Applications ◽  
Vlsi Chip ◽  
Bin Packing Problem

In this paper, we study the two-dimensional geometrical bin packing problem (2DBP): given a list of rectangles, provide a packing of all these into the smallest possible number of unit bins without rotating the rectangles. Beyond its theoretical appeal, this problem has many practical applications, for example in print layout and VLSI chip design. We present a 2-approximate algorithm, which improves over the previous best known ratio of 3, matches the best results for the problem where rotations are allowed and also matches the known lower bound of approximability. Our approach makes strong use of a PTAS for a related 2D knapsack problem and a new algorithm that can pack instances into two bins if OPT = 1.

scholarly journals Discovering Subsequence Patterns for Next POI Recommendation

2020 ◽  
Author(s):  
Kangzhi Zhao ◽  
Yong Zhang ◽  
Hongzhi Yin ◽  
Jin Wang ◽  
Kai Zheng ◽  
...  
Keyword(s):  
Power Law ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Location Based Services ◽  
Sequential Patterns ◽  
Economic Activities ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Latent Structures ◽  
Real World Datasets

Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user's check-in sequence but ignore the subsequence patterns that often represent the socio-economic activities or coherence of preference of the users. However, it is challenging to integrate the semantic subsequences due to the difficulty to predefine the granularity of the complex but meaningful subsequences. In this paper, we propose Adaptive Sequence Partitioner with Power-law Attention (ASPPA) to automatically identify each semantic subsequence of POIs and discover their sequential patterns. Our model adopts a state-based stacked recurrent neural network to hierarchically learn the latent structures of the user's check-in sequence. We also design a power-law attention mechanism to integrate the domain knowledge in spatial and temporal contexts. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.

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