Fine-Grained User Location Prediction using Meta-Path Context with Attention Mechanism

2021 ◽  
Author(s):  
Zhixiao Wang ◽  
Wenyao Yan ◽  
Ang Gao
Keyword(s):  
Social Networks ◽  
Positive Sample ◽  
Data Sets ◽  
Suggested Approach ◽  
Heterogeneous Information ◽  
Fine Grained ◽  
High Efficient ◽  
Meta Path ◽  
Location Based Social Networks ◽  
User Location

The prevalence of Location-Based Social Networks (LBSNs) significantly improves the location-aware capability of services by providing Geo-tagged information. Relied on a great number of user check-in data in the location-based social networks, their essential mobility modes are able to be comprehensively studied, which is basic for forecasting the next venue where a specific user is going to visit considering his relevant historical check-in data. Since there exist different kinds of nodes and interactions between nodes, these information could be look upon as a network that is made up of heterogeneous information. In this network a few of different semantic meta paths could be obtained. Enlightened from the competitive advantage of embedding method relied upon meta-path contexts in the heterogeneous information network, we study a joint deep learning scheme exploring different meta-path context information to forecast fine-grained location. In order to capture different semantics in a user-location interaction, we adopt a simple but high-efficient attention method to learn the meta-path importance or weights. In the terms of model optimization, considering we have only positive sample data and there exists intrinsically latent feedback in check-in information, herein a pairwise learning method is utilized for maximizing the margin between visited and invisible venues. Experiment in different data-sets validate the competitive performance of the suggested approach under different assessment criterion.

scholarly journals Estratégias para Aprimorar a Diversidade Categórica e Geográfica de Sistemas de Recomendação de POIs

2020 ◽  
Author(s):  
Rodrigo Carvalho ◽  
Leonardo Rocha
Keyword(s):  
Social Networks ◽  
User Satisfaction ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Data Sets ◽  
Processing Strategy ◽  
Post Processing ◽  
Practical Utility ◽  
Points Of Interest ◽  
Location Based Social Networks

Currently, so-called Recommendation Systems (SRs) have been used to assist users in discovering relevant Points of Interest (POIs) on Location-Based Social Networks (LBSN), such as FourSquare and Yelp. Given the main challenges of data-sparse and geographic influence in this scenario, most of the work on POI recommendations has focused only on improving the effectiveness (i.e. accuracy) of the systems. However, there is a growing consensus that just effectiveness is not sufficient to assess the practical utility of these systems. In real scenarios, categorical and geographic diversities were identified as the main complementary dimensions for assessing user satisfaction and the usefulness of recommendations. The works in the literature are concentrated on only one of these concepts. In this work, we propose a new post-processing strategy, which combines these concepts in order to improve the user’s interest in POIs. Our experimental results in the Yelp data sets show that our strategy can improve user satisfaction, considering different SRs and multiple diversification metrics. Our method is capable of improving diversity by up to 120 % without significant losses in terms of effectiveness.

scholarly journals Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

2019 ◽  
Vol 1284 ◽  
pp. 012031
Author(s):  
Zhixiao Wang ◽  
Wenyao Yan ◽  
Wendong Wang ◽  
Ang Gao ◽  
Lei Yu ◽  
...  
Keyword(s):  
Attention Mechanism ◽  
Location Prediction ◽  
Fine Grained ◽  
Meta Path ◽  
User Location

scholarly journals Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

2019 ◽  
Vol 8 (9) ◽  
pp. 406 ◽  
Author(s):  
Khazaei ◽  
Alimohammadi
Keyword(s):  
Social Networks ◽  
Random Walk ◽  
Spatial Proximity ◽  
Context Aware ◽  
Human Beings ◽  
Location Recommendation ◽  
Aware Group ◽  
Location Based Social Networks ◽  
Group Profile ◽  
User Location

Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times.

Location-Based Social Networks

2012 ◽  
pp. 243-253 ◽  
Author(s):  
Declan Traynor ◽  
Kevin Curran
Keyword(s):  
Social Networks ◽  
Real World ◽  
Location Based Services ◽  
Software Systems ◽  
Privacy And Security ◽  
Contextual Data ◽  
New Form ◽  
Location Based Social Networks ◽  
User Location ◽  
Exciting Application

The ability to gather and manipulate real world contextual data, such as user location, in modern software systems presents opportunities for new and exciting application areas. A key focus among those working in the area of Location-Based services today has been the creation of social networks which allow mobile device users to exchange details of their personal location as a key point of interaction. While the initial interest in these services has been exceptionally high, they are plagued by the same challenges as all Location Based services, regarding the privacy and security of users and their data. This chapter aims to investigate the area of Location-Based Social Networks (LBSNs), with a view to documenting how they contribute to a new form of expertise due to the now accurate knowledge of where people are actually located at a moment in time.

scholarly journals Venue2Vec: An Efficient Embedding Model for Fine-Grained User Location Prediction in Geo-Social Networks

2020 ◽  
Vol 14 (2) ◽  
pp. 1740-1751 ◽  
Author(s):  
Shuai Xu ◽  
Jiuxin Cao ◽  
Phil Legg ◽  
Bo Liu ◽  
Shancang Li
Keyword(s):  
Social Networks ◽  
Location Prediction ◽  
Fine Grained ◽  
User Location

Incorporating heterogeneous information in deep learning with informative meta-paths for community recommendations

2021 ◽  
pp. 016555152110474
Author(s):  
Weiwei Deng ◽  
Wei Du ◽  
Cong Han
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Superior Performance ◽  
Data Sets ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Communities Of Interest ◽  
Meta Path ◽  
Heterogeneous Interactions ◽  
Deep Learning Model

Communities of interest promote knowledge sharing and discovery in social network platforms. However, platform users face difficulties of finding suitable communities, given their increasing number. Although recommendations have been proposed to help users find communities of interest, these methods ignore or exclude heterogeneous interactions between users and communities. In addition, widely used meta-paths help capture the complex semantic relation among entities but heavily rely on domain knowledge. In this study, we propose a novel recommendation model based on informative meta-path discovery in heterogeneous information networks and deep learning. Users, communities, relevant items and their relations are considered as entities in a heterogeneous information network, from where informative meta-paths are extracted on the basis of information theory to measure user-community similarities. Finally, similarities are incorporated in a deep learning model to predict whether target users join candidate communities. The proposed recommendation model is evaluated and compared against baseline methods using two data sets. Results demonstrate the superior performance of the present model in terms of precision, recall and F score.

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.

Predicting POI Visits in a Heterogeneous Location-Based Social Network

2016 ◽  
Vol 20 (6) ◽  
pp. 882-892 ◽  
Author(s):  
Zih-Syuan Wang ◽  
◽  
Jing-Fu Juang ◽  
Wei-Guang Teng
Keyword(s):  
Social Network ◽  
Prediction Model ◽  
Good Prediction ◽  
Heterogeneous Information ◽  
Practical Applications ◽  
Point Of Interest ◽  
Topological Features ◽  
Meta Path ◽  
Location Based Social Network ◽  
Location Based Social Networks

A point of interest (POI) is a specific location that people may find useful or interesting, such as restaurants, stores, attractions, and hotels. With the recent proliferation of location-based social networks (LBSN), numerous users gather to interact and share information on various POIs. POI recommendations have become a crucial issue because it not only helps users to learn about new places but also gives LBSN providers chances to post POI advertisements. As we utilize a heterogeneous information network to represent an LBSN in this work, POI recommendations are remodeled as a link prediction problem, which is significant in the field of social network analysis. Moreover, we propose to utilize the meta-path-based approach to extract implicit but potentially useful relationships between a user and a POI. Then, the extracted topological features are used to construct a prediction model with appropriate data classification techniques. In our experiments, the Yelp dataset is utilized as our testbed for performance evaluation purposes. The results show that our prediction model is of good prediction quality in practical applications.

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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