scholarly journals Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zengwei Zheng ◽  
Yuanyi Chen ◽  
Sinong Chen ◽  
Lin Sun ◽  
Dan Chen
Keyword(s):  
Indoor Localization ◽  
User Preference ◽  
Shopping Mall ◽  
Behaviour Pattern ◽  
Indoor Space ◽  
Point Of Interest ◽  
Location Aware ◽  
Poi Recommendation ◽  
Shopping Experience ◽  
Two Phases

Indoor shopping trajectories provide us with a new approach to understanding user’s behaviour pattern in urban shopping mall, which can be derived from user-generated WiFi logs using indoor localization technology. In this paper, we propose a location-aware Point-of-Interest (POI) recommendation service in urban shopping mall that offers a user a set of indoor POIs by considering both personal interest and location preference. The POI recommendation service cannot only improve user’s shopping experience but also help the store owner better understand user’s shopping preference and intent. Specifically, the proposed method consists of two phases: offline modelling and online recommendation. The offline modelling phase is designed to learn user preference by mining his/her historical shopping trajectories. The online recommendation phase automatically produces top-k recommended POIs based on the learnt preference. To demonstrate the utility of our proposed approach, we have performed a comprehensive experiment evaluation on a real-world dataset collected by 468 users over 33 days. The experimental results show that the proposed recommendation service achieves much better recommendation performance than several existing benchmark methods.

scholarly journals Discovering Influential Positions in RFID-Based Indoor Tracking Data

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 330
Author(s):  
Ye Jin ◽  
Lizhen Cui
Keyword(s):  
Moving Objects ◽  
Indoor Localization ◽  
Rapid Development ◽  
Shopping Mall ◽  
Position Tracking ◽  
Tracking Data ◽  
H Index ◽  
Indoor Space ◽  
Indoor Tracking ◽  
Indoor Position Tracking

The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average.

scholarly journals Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

2021 ◽  
Author(s):  
Yang Li ◽  
Tong Chen ◽  
Yadan Luo ◽  
Hongzhi Yin ◽  
Zi Huang
Keyword(s):  
Social Networks ◽  
State Of The Art ◽  
User Preference ◽  
Sequential Patterns ◽  
Preference Modelling ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Multi Level ◽  
Real World Datasets ◽  
Location Based Social Networks

Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.

Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation

2019 ◽  
Vol 16 (4) ◽  
pp. 40-52 ◽  
Author(s):  
Jun Zeng ◽  
Feng Li ◽  
Xin He ◽  
Junhao Wen
Keyword(s):  
Social Networks ◽  
User Preference ◽  
Integrated Analysis ◽  
Geographical Distance ◽  
Point Of Interest ◽  
Social Reputation ◽  
Poi Recommendation ◽  
Interest Level ◽  
Location Based Social Networks ◽  
Better Than

Point of interest (POI) recommendation is a significant task in location-based social networks (LBSNs), e.g., Foursquare, Brightkite. It helps users explore the surroundings and help POI owners increase income. While several researches have been proposed for the recommendation services, it lacks integrated analysis on POI recommendation. In this article, the authors propose a unified recommendation framework, which fuses personalized user preference, geographical influence, and social reputation. The TF-IDF method is adopted to measure the interest level and contribution of locations when calculating the similarity between users. Geographical influence includes geographical distance and location popularity. The authors find friends in Brightkite share low common visited POIs. It means friends' interests may vary greatly. Instead of directly getting recommendations from so-called friends in LBSN, the users attain recommendation from others according to their reputation. Finally, experimental results on real-world dataset demonstrate that the proposed method performs much better than other recommendation methods.

scholarly journals The study of 3Rs – Reuse, Repair, and Redesign at Swedish recycling mall

2019 ◽  
Vol 70 (06) ◽  
pp. 552-556
Author(s):  
MANOJ KUMAR PARAS ◽  
LARS HEDEGÅRD ◽  
ANTONELA CURTEZA ◽  
RUDRAJEET PAL ◽  
YAN CHEN ◽  
...  
Keyword(s):  
Value Chain ◽  
Construction Material ◽  
New Products ◽  
Management Team ◽  
Shopping Mall ◽  
Food Outlets ◽  
Shopping Experience ◽  
Unique Experience ◽  
Two Phases ◽  
Sustainable Practice

The shopping mall concept has emerged to provide unique mall facets to satisfy consumers that search for the ultimate shopping experience. Under one roof different sellers are assembled together with food outlets and entertainment to fulfill the requirements of consumers. Gradually an awareness of over-consumption has risen together which calls for reuse activities that reduce the consumption of new products. As an answer to the concept of a mall for sustainable practice, a recycling mall has been developed in Eskilstuna, Sweden. This study has been undertaken to understand the practice of the recycling mall and its encompassing reverse value chain activities. This is primarily done in two phases i.e., first to understand the backend operations by visiting collecting and sorting facilities and secondly to comprehend the perspectives of the management team by interviewing them. The findings from the current study suggest that the unique concept of recycling mall create a positive awareness among the customers to reuse, repair and redesign used products. An individual gets a unique experience to donate and purchase clothes, sports equipment, and construction material under one roof

Point-of-Interest Recommendation Based on User Contextual Behavior Semantics

2019 ◽  
Vol 29 (11n12) ◽  
pp. 1781-1799
Author(s):  
Dongjin Yu ◽  
Kaihui Xu ◽  
Dongjing Wang ◽  
Ting Yu ◽  
Wanqing Li
Keyword(s):  
Learning To Rank ◽  
Real Data ◽  
User Preference ◽  
Heterogeneous Information ◽  
Fine Grained ◽  
Point Of Interest ◽  
Factorization Model ◽  
Poi Recommendation ◽  
Weighted Matrix ◽  
Meta Path

By suggesting new visiting places, point-of-interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the data sparsity and complexity of user check-in behavior still pose big challenges to accurate personalized POI recommendation. To tackle these problems, in this paper, we propose a POI recommendation model named HeteGeoRankRec based on user contextual behavior semantics. First, we employ the meta-path of heterogeneous information network (HIN) to represent the complex semantic relationship among users and POIs. Second, we introduce different context constraints (such as time and weather) into the meta-path, to reveal the fine-grained user behavioral features. Afterwards, we propose a weighted matrix factorization model which considers the influence of geographical distance through the user–POI semantic correlativity matrices generated by multiple meta-paths. Finally, we present a fusion method based on learning to rank, which unifies the recommendation results of different meta-paths as the final user preference. The experiments on the real data collected from Foursquare demonstrate that HeteGeoRankRec has the better performance than the state-of-the-art baselines.

scholarly journals A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Jin Zhu ◽  
Dayu Cheng ◽  
Weiwei Zhang ◽  
Ci Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Semantic Information ◽  
Edit Distance ◽  
Similarity Measures ◽  
Indoor Positioning ◽  
Synthetic Dataset ◽  
Shopping Mall ◽  
Indoor Space ◽  
Trajectory Similarity ◽  
Indoor Spaces

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

scholarly journals Why We Go Where We Go: Profiling User Decisions on Choosing POIs

2020 ◽  
Author(s):  
Renjun Hu ◽  
Xinjiang Lu ◽  
Chuanren Liu ◽  
Yanyan Li ◽  
Hao Liu ◽  
...  
Keyword(s):  
Key Factors ◽  
Real World Data ◽  
Decision Factors ◽  
Point Of Interest ◽  
Learning Factor ◽  
Poi Recommendation ◽  
Key Factor ◽  
Selection Of ◽  
Decision Structures

While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people's decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to identify key factors, while preserving decision structures seamlessly, via a novel scalar projection maximization objective. Exactly solving the objective is non-trivial due to a sparsity constraint. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analyses.

scholarly journals GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network

2020 ◽  
Vol 5 (4) ◽  
pp. 433-447
Author(s):  
Shiwen Wu ◽  
Yuanxing Zhang ◽  
Chengliang Gao ◽  
Kaigui Bian ◽  
Bin Cui
Keyword(s):  
Mobile Networks ◽  
State Of The Art ◽  
Economic Interest ◽  
Location Based Service ◽  
Vast Number ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Core Issue ◽  
Real World Datasets ◽  
Content Aware

Abstract The advances of mobile equipment and localization techniques put forward the accuracy of the location-based service (LBS) in mobile networks. One core issue for the industry to exploit the economic interest of the LBSs is to make appropriate point-of-interest (POI) recommendation based on users’ interests. Today, the LBS applications expect the recommender systems to recommend the accurate next POI in an anonymous manner, without inquiring users’ attributes or knowing the detailed features of the vast number of POIs. To cope with the challenge, we propose a novel attentive model to recommend appropriate new POIs for users, namely Geographical Attentive Recommendation via Graph (GARG), which takes full advantage of the collaborative, sequential and content-aware information. Unlike previous strategies that equally treat POIs in the sequence or manually define the relationships between POIs, GARG adaptively differentiates the relevance of POIs in the sequence to the prediction, and automatically identifies the POI-wise correlation. Extensive experiments on three real-world datasets demonstrate the effectiveness of GARG and reveal a significant improvement by GARG on the precision, recall and mAP metrics, compared to several state-of-the-art baseline methods.

scholarly journals An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

2019 ◽  
Vol 8 (8) ◽  
pp. 355 ◽  
Author(s):  
Chunyang Liu ◽  
Jiping Liu ◽  
Jian Wang ◽  
Shenghua Xu ◽  
Houzeng Han ◽  
...  
Keyword(s):  
Behavioral Intention ◽  
Early Stage ◽  
Time Interval ◽  
Time Step ◽  
Attention Model ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Real World Datasets ◽  
Current Sequence ◽  
Gated Recurrent Unit

Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user’s preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user’s main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user’s sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user’s main behavioral intention in the user’s check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.

scholarly journals Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chunyang Liu ◽  
Chao Liu ◽  
Haiqiang Xin ◽  
Jian Wang ◽  
Jiping Liu ◽  
...  
Keyword(s):  
Metric Space ◽  
Matrix Factorization ◽  
Euclidean Distance ◽  
Large Scale ◽  
Inner Product ◽  
Interaction Matrix ◽  
Distance Metric ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Real World Datasets

Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extreme sparsity of check-in data makes it challenging to capture users’ movement preferences accurately. In this paper, we propose a joint geosequential preference and distance metric factorization framework, called GeoSeDMF, for POI recommendation. First, we introduce a distance metric factorization method that is capable of learning users’ personalized preferences from a position and distance perspective in the metric space. Specifically, we convert the user-POI interaction matrix into a distance matrix and factorize it into user and POI dense embeddings. Additionally, we measure users’ personalized preference for the POI by using the Euclidean distance metric instead of the inner product. Then, we model the users’ geospatial preference by applying a geographic weight coefficient and model the users’ sequential preference by using the Euclidean distance of continuous check-in locations. Moreover, a pointwise loss strategy and AdaGrad algorithm are adopted to optimize the positions and relationships of users and POIs in a metric space. Finally, experimental results on three large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.

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