scholarly journals POI Recommendation Method Using Deep Learning in Location-Based Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Liu ◽  
An-bo Wu
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Short Term Memory ◽  
Large Data ◽  
Short Term ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Bayesian Personalized Ranking ◽  
Location Based Social Networks ◽  
Current Sequence

To solve the problems of large data sparsity and lack of negative samples in most point of interest (POI) recommendation methods, a POI recommendation method based on deep learning in location-based social networks is proposed. Firstly, a bidirectional long-short-term memory (Bi-LSTM) attention mechanism is designed to give different weights to different parts of the current sequence according to users’ long-term and short-term preferences. Then, the POI recommendation model is constructed, the sequence state data of the encoder is input into Bi-LSTM-Attention to get the attention representation of the current POI check-in sequence, and the Top- N recommendation list is generated after the decoder processing. Finally, a negative sampling method is proposed to obtain an effective negative sample set, which is used to improve the calculation of the Bayesian personalized ranking loss function. The proposed method is demonstrated experimentally on Foursquare and Gowalla datasets. The experimental results show that the proposed method has better accuracy, recall, and F1 value than other comparison methods.

scholarly journals Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation

2018 ◽  
Author(s):  
Hao Wang ◽  
Huawei Shen ◽  
Wentao Ouyang ◽  
Xueqi Cheng
Keyword(s):  
Social Networks ◽  
Real World ◽  
Fundamental Problem ◽  
Physical Distance ◽  
Experimental Results ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Movie Recommendation ◽  
Real World Datasets ◽  
Location Based Social Networks

Point-of-interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geo-susceptibility of POI, and their physical distance. Geo-influence captures POI?s capacity at exerting geographical influence to other POIs, and geo-susceptibility reflects POI?s propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation.

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.

scholarly journals Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

2019 ◽  
Vol 33 ◽  
pp. 5877-5884 ◽  
Author(s):  
Pengpeng Zhao ◽  
Haifeng Zhu ◽  
Yanchi Liu ◽  
Jiajie Xu ◽  
Zhixu Li ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Service Providers ◽  
The State ◽  
Short Term ◽  
Temporal Relationships ◽  
Poi Recommendation ◽  
Temporal Intervals ◽  
Spatio Temporal ◽  
Real World Datasets ◽  
Location Based Social Networks

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.

scholarly journals POI recommendation method using LSTM-attention in LBSN considering privacy protection

2021 ◽  
Author(s):  
Kun Wang ◽  
Xiaofeng Wang ◽  
Xuan Lu
Keyword(s):  
Deep Learning ◽  
Privacy Protection ◽  
Geographical Location ◽  
User Preferences ◽  
Sequence Length ◽  
Short Term ◽  
Network Information ◽  
Poi Recommendation ◽  
Location Based Social Networks ◽  
Better Than

AbstractAiming at the problems of traditional point of interest (POI), such as sparse data, lack of negative feedback, and dynamic and periodic changes of user preferences, a POI recommendation method using deep learning in location-based social networks (LBSN) considering privacy protection is proposed. First, the idea of Embedding is used to quantify the user information, friend relationship, POI information, and so on, so as to obtain the internal relationship of the location. Then, based on the user's history and current POI check-in sequence set, the long- and short-term attention mechanism (LSA) is constructed, and the quantified information is used as the input of LSA to better capture the user's long-term and short-term preferences. Finally, the social network information and semantic information are fitted in different input layers, and the time and geographical location information of user's historical behavior are used to recommend the next POI for users. Gowalla and Brightkite datasets are used to demonstrate the proposed method. The results show that the performance of the proposed method is better than other comparison methods under different sparsity, location sequence length, and embedding length. When the number of iterations is 500, the recommended method tends to be stable, and the accuracy is 0.27. Moreover, the recommendation time of the proposed method is less than 130 ms, which is better than other comparative deep learning methods.

scholarly journals Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks

2021 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Hang Zhang ◽  
Mingxin Gan ◽  
Xi Sun
Keyword(s):  
Social Networks ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Repeat Visit ◽  
Preference Evolution ◽  
Attenuation Mechanism ◽  
Mobility Behavior ◽  
Location Based Social Networks

In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods.

scholarly journals Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking

2017 ◽  
Author(s):  
Jing He ◽  
Xin Li ◽  
Lejian Liao
Keyword(s):  
Ranking List ◽  
Point Of Interest ◽  
Spatial Influence ◽  
Poi Recommendation ◽  
Bayesian Personalized Ranking ◽  
Real World Datasets ◽  
Location Based Social Networks ◽  
Luce Model ◽  
Personalized Ranking ◽  
High Computational Complexity

Next Point-of-interest (POI) recommendation has become an important task for location-based social networks (LBSNs). However, previous efforts suffer from the high computational complexity and the transition pattern between POIs has not been well studied. In this paper, we propose a two-fold approach for next POI recommendation. First, the preferred next category is predicted by using a third-rank tensor optimized by a Listwise Bayesian Personalized Ranking (LBPR) approach. Specifically we introduce two functions, namely Plackett-Luce model and cross entropy, to generate the likelihood of ranking list for posterior computation. Then POI candidates filtered by the predicated category are ranked based on the spatial influence and category ranking influence. Extensive experiments on two real-world datasets demonstrate the significant improvements of our methods over several state-of-the-art methods.

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 Discovering Travel Community for POI Recommendation on Location-Based Social Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Tang ◽  
Dandan Cai ◽  
Zongtao Duan ◽  
Junchi Ma ◽  
Meng Han ◽  
...  
Keyword(s):  
New York ◽  
Social Networks ◽  
Transportation Systems ◽  
Variable Order ◽  
Linear Discriminant ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Recommendation Algorithms ◽  
Real World Datasets ◽  
Location Based Social Networks

Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.

scholarly journals A Joint Deep Recommendation Framework for Location-Based Social Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Omer Tal ◽  
Yang Liu
Keyword(s):  
Neural Networks ◽  
Social Networks ◽  
Deep Learning ◽  
Spatial Data ◽  
Mean Squared Error ◽  
Multilayer Perceptrons ◽  
Point Of Interest ◽  
Exciting Opportunity ◽  
Multiple State ◽  
Location Based Social Networks

Location-based social networks, such as Yelp and Tripadvisor, which allow users to share experiences about visited locations with their friends, have gained increasing popularity in recent years. However, as more locations become available, the need for accurate systems able to present personalized suggestions arises. By providing such service, point-of-interest recommender systems have attracted much interest from different societies, leading to improved methods and techniques. Deep learning provides an exciting opportunity to further enhance these systems, by utilizing additional data to understand users’ preferences better. In this work we propose Textual and Contextual Embedding-based Neural Recommender (TCENR), a deep framework that employs contextual data, such as users’ social networks and locations’ geo-spatial data, along with textual reviews. To make best use of these inputs, we utilize multiple types of deep neural networks that are best suited for each type of data. TCENR adopts the popular multilayer perceptrons to analyze historical activities in the system, while the learning of textual reviews is achieved using two variations of the suggested framework. One is based on convolutional neural networks to extract meaningful data from textual reviews, and the other employs recurrent neural networks. Our proposed network is evaluated over the Yelp dataset and found to outperform multiple state-of-the-art baselines in terms of accuracy, mean squared error, precision, and recall. In addition, we provide further insight into the design selections and hyperparameters of our recommender system, hoping to shed light on the benefit of deep learning for location-based social network recommendation.

scholarly journals Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 279 ◽  
Author(s):  
Mingxin Gan ◽  
Ling Gao
Keyword(s):  
Social Networks ◽  
Extensive Study ◽  
Psychological Effects ◽  
Data Set ◽  
Point Of Interest ◽  
Best Value ◽  
Poi Recommendation ◽  
Location Based Social Networks ◽  
Near Future ◽  
Daily Travel

Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days.

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