Context-aware POI Recommendation using Neutrosophic Set for Mobile Edge Computing

Abstract With the rapid development of mobile communication technology, there is a growing demand for high-quality point of interest(POI) recommendation. The POIs visited by users only account for a very small proportion. Thus traditional POI recommendation method is vulnerable to data sparsity and lacks a clear and effective explanation for POI ranking result. The POI selection made by the user is influenced by various contextual attributes. The challenge lies in representing accurately and aggregating multiple contextual information efficiently. We transform the POI recommendation into a contextual multi-attribute decision problem based on the neutrosophic set (NS) which is suitable for representing fuzzy decision information. We establish a unified framework of contextual information. Firstly, we propose a contextual multi-attribute NS transformation model of POI, including the NS model for single-dimensional attributes and the NS model for multi-dimensional attributes. And then through the aggregation of multi attribute NS, the POI that best conforms to user's preferences is recommended. Finally, the experimental results based on the Yelp dataset show that the proposed strategy performs better than the typical POI recommendation method in NDCG, accuracy, and recall rate.

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Dynamic Recommendation of POI Sequence Responding to Historical Trajectory

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8100433 ◽

2019 ◽

Vol 8 (10) ◽

pp. 433 ◽

Cited By ~ 4

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.

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IGTP: A Next Point-of-Interest Recommendation Method that Integrates Geospatial and Temporal Preferences

10.21203/rs.3.rs-752826/v1 ◽

2021 ◽

Author(s):

Xu Jiao ◽

Yingyuan Xiao ◽

Wenguang Zheng ◽

Ke Zhu

Keyword(s):

Rapid Development ◽

Geographic Distance ◽

Decision Making Process ◽

Point Of Interest ◽

Current Location ◽

Poi Recommendation ◽

Target User ◽

Location Based Social Networks ◽

The Individual ◽

Temporal Preferences

Abstract With the rapid development of location-based social networks(LBSNs), point-of-interest(POI) recommendation has become an important way to meet the personalized needs of users. The purpose of POI recommendation is to provide personalized POI recommendation services for users. However, general POI recommendations cannot meet the individual needs of users. This is mainly because the decision-making process for users to choose POIs is very complicated and will be affected by various user contexts such as time, location, etc. This paper proposes a next POI recommendation method that integrates geospatial and temporal preferences, called IGTP. Compared with general POI recommendation, IGTP can provide more personalized recommendations for users according to their context information. First, IGTP uses users' preferences information to model users' check-in histories to effectively overcome the challenge of extremely sparse check-in data. Secondly, IGTP takes into account the geographic distance and density factors that affect people's choice of POIs, and limits POIs to be recommended to the potential activitive area centered on the current location of the target user. Finally, IGTP integrates geospatial and users' temporal preferences information into a unified recommendation process. Compared with six advanced baseline methods, the experimental results demonstrate that IGTP achieves much better performance.

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Contextualized Point-of-Interest Recommendation

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/344 ◽

2020 ◽

Author(s):

Peng Han ◽

Zhongxiao Li ◽

Yong Liu ◽

Peilin Zhao ◽

Jing Li ◽

...

Keyword(s):

Common Property ◽

Recommendation System ◽

Contextual Information ◽

Optimization Method ◽

Graph Laplacian ◽

Local Context ◽

Context Information ◽

Point Of Interest ◽

Poi Recommendation ◽

Global And Local

Point-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i.e., global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.

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P3OI-MELSH: Privacy Protection Target Point of Interest Recommendation Algorithm Based on Multi-Exploring Locality Sensitive Hashing

Frontiers in Neurorobotics ◽

10.3389/fnbot.2021.660304 ◽

2021 ◽

Vol 15 ◽

Author(s):

Desheng Liu ◽

Linna Shan ◽

Lei Wang ◽

Shoulin Yin ◽

Hui Wang ◽

...

Keyword(s):

Social Network ◽

Privacy Protection ◽

Rapid Development ◽

Locality Sensitive Hashing ◽

Target Point ◽

Interest Points ◽

Recommendation Algorithm ◽

Original Algorithm ◽

Point Of Interest ◽

Poi Recommendation

With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by users in the era of big data, improve user experience, but also help merchants quickly find target users and achieve accurate marketing. Most of the works are based on users' check-in history and social network data to model users' personalized preferences for interest points, and recommend interest points through collaborative filtering and other recommendation technologies. However, in the check-in history, the multi-source heterogeneous information (including the position, category, popularity, social, reviews) describes user activity from different aspects which hides people's life style and personal preference. However, the above methods do not fully consider these factors' combined action. Considering the data privacy, it is difficult for individuals to share data with others with similar preferences. In this paper, we propose a privacy protection point of interest recommendation algorithm based on multi-exploring locality sensitive hashing (LSH). This algorithm studies the POI recommendation problem under distributed system. This paper introduces a multi-exploring method to improve the LSH algorithm. On the one hand, it reduces the number of hash tables to decrease the memory overhead; On the other hand, the retrieval range on each hash table is increased to reduce the time retrieval overhead. Meanwhile, the retrieval quality is similar to the original algorithm. The proposed method uses modified LSH and homomorphic encryption technology to assist POI recommendation which can ensure the accuracy, privacy and efficiency of the recommendation algorithm, and it verifies feasibility through experiments on real data sets. In terms of root mean square error (RMSE), mean absolute error (MAE) and running time, the proposed method has a competitive advantage.

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Exploiting Spatial and Temporal for Point of Interest Recommendation

Complexity ◽

10.1155/2018/6928605 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Jinpeng Chen ◽

Wen Zhang ◽

Pei Zhang ◽

Pinguang Ying ◽

Kun Niu ◽

...

Keyword(s):

Probabilistic Method ◽

Mobility Patterns ◽

Unified Framework ◽

Spatiotemporal Information ◽

Point Of Interest ◽

Poi Recommendation ◽

Target User ◽

Temporal Influence ◽

Real World Datasets ◽

Location Based Social Networks

An increasing number of users have been attracted by location-based social networks (LBSNs) in recent years. Meanwhile, user-generated content in online LBSNs like spatial, temporal, and social information provides an ever-increasing chance to study the human behavior movement from their spatiotemporal mobility patterns and spawns a large number of location-based applications. For instance, one of such applications is to produce personalized point of interest (POI) recommendations that users are interested in. Different from traditional recommendation methods, the recommendations in LBSNs come with two vital dimensions, namely, geographical and temporal. However, previously proposed methods do not adequately explore geographical influence and temporal influence. Therefore, fusing geographical and temporal influences for better recommendation accuracy in LBSNs remains potential. In this work, our aim is to generate a top recommendation list of POIs for a target user. Specially, we explore how to produce the POI recommendation by leveraging spatiotemporal information. In order to exploit both geographical and temporal influences, we first design a probabilistic method to initially detect users’ spatial orientation by analyzing visibility weights of POIs which are visited by them. Second, we perform collaborative filtering by detecting users’ temporal preferences. At last, for making the POI recommendation, we combine the aforementioned two approaches, that is, integrating the spatial and temporal influences, to construct a unified framework. Our experimental results on two real-world datasets indicate that our proposed method outperforms the current state-of-the-art POI recommendation approaches.

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Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou

Sustainability ◽

10.3390/su13020444 ◽

2021 ◽

Vol 13 (2) ◽

pp. 444

Author(s):

Xucai Zhang ◽

Yeran Sun ◽

Ting On Chan ◽

Ying Huang ◽

Anyao Zheng ◽

...

Keyword(s):

Rapid Development ◽

Ordinary Least Squares ◽

Information And Communications Technology ◽

Time Data ◽

Location Data ◽

Point Of Interest ◽

Quality Life ◽

City Construction ◽

The Impact ◽

The City

Urban vibrancy contributes towards a successful city and high-quality life for people as one of its vital elements. Therefore, the association between service facilities and vibrancy is crucial for urban managers to understand and improve city construction. Moreover, the rapid development of information and communications technology (ICT) allows researchers to easily and quickly collect a large volume of real-time data generated by people in daily life. In this study, against the background of emerging multi-source big data, we utilized Tencent location data as a proxy for 24-h vibrancy and adopted point-of-interest (POI) data to represent service facilities. An analysis framework integrated with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR) models is proposed to explore the spatiotemporal relationships between urban vibrancy and POI-based variables. Empirical results show that (1) spatiotemporal variations exist in the impact of service facilities on urban vibrancy across Guangzhou, China; and (2) GTWR models exhibit a higher degree of explanatory capacity on vibrancy than the OLS models. In addition, our results can assist urban planners to understand spatiotemporal patterns of urban vibrancy in a refined resolution, and to optimize the resource allocation and functional configuration of the city.

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Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection

Journal Of Big Data ◽

10.1186/s40537-020-00391-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Asmaa El Hannani ◽

Rahhal Errattahi ◽

Fatima Zahra Salmam ◽

Thomas Hain ◽

Hassan Ouahmane

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Error Detection ◽

State Of The Art ◽

Rapid Development ◽

Unified Framework ◽

Human Machine Interaction ◽

Detection Analysis ◽

Extensive Evaluation ◽

Effectiveness And Efficiency

AbstractSpeech based human-machine interaction and natural language understanding applications have seen a rapid development and wide adoption over the last few decades. This has led to a proliferation of studies that investigate Error detection and classification in Automatic Speech Recognition (ASR) systems. However, different data sets and evaluation protocols are used, making direct comparisons of the proposed approaches (e.g. features and models) difficult. In this paper we perform an extensive evaluation of the effectiveness and efficiency of state-of-the-art approaches in a unified framework for both errors detection and errors type classification. We make three primary contributions throughout this paper: (1) we have compared our Variant Recurrent Neural Network (V-RNN) model with three other state-of-the-art neural based models, and have shown that the V-RNN model is the most effective classifier for ASR error detection in term of accuracy and speed, (2) we have compared four features’ settings, corresponding to different categories of predictor features and have shown that the generic features are particularly suitable for real-time ASR error detection applications, and (3) we have looked at the post generalization ability of our error detection framework and performed a detailed post detection analysis in order to perceive the recognition errors that are difficult to detect.

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

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/478 ◽

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.

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Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽

10.1155/2020/6643888 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Hui Ning ◽

Qian Li

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Contextual Information ◽

Rapid Development ◽

Theoretical Research ◽

Context Information ◽

Music Recommendation ◽

Recommendation Algorithm ◽

Points Of Interest ◽

Popularity Prediction

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.

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

Data Science and Engineering ◽

10.1007/s41019-020-00135-z ◽

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.

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