CHA: Categorical Hierarchy-based Attention for Next POI Recommendation

2022 ◽  
Vol 40 (1) ◽  
pp. 1-22
Author(s):  
Hongyu Zang ◽  
Dongcheng Han ◽  
Xin Li ◽  
Zhifeng Wan ◽  
Mingzhong Wang
Keyword(s):  
State Of The Art ◽  
Behavior Pattern ◽  
Knowledge Graph ◽  
Insufficient Data ◽  
Large Margin ◽  
Temporal Decay ◽  
Business Operations ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Location Based Social Networks

Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.

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.

Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning Approach

2022 ◽  
Vol 40 (2) ◽  
pp. 1-22
Author(s):  
Yue Cui ◽  
Hao Sun ◽  
Yan Zhao ◽  
Hongzhi Yin ◽  
Kai Zheng
Keyword(s):  
Fundamental Problem ◽  
Knowledge Graph ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Meta Learning ◽  
Knowledge Graphs ◽  
Spatio Temporal ◽  
Sequential Information ◽  
Location Based Social Networks ◽  
Gated Recurrent Units

Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.

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 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.

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.

scholarly journals MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation

2021 ◽  
Author(s):  
Huimin Sun ◽  
Jiajie Xu ◽  
Kai Zheng ◽  
Pengpeng Zhao ◽  
Pingfu Chao ◽  
...  
Keyword(s):  
State Of The Art ◽  
Cold Start ◽  
User Preferences ◽  
Location Based Services ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Meta Learning ◽  
Optimized Model ◽  
Real World Datasets ◽  
Cold Start Problem

Next Point-of-Interest (POI) recommendation is of great value for location-based services. Existing solutions mainly rely on extensive observed data and are brittle to users with few interactions. Unfortunately, the problem of few-shot next POI recommendation has not been well studied yet. In this paper, we propose a novel meta-optimized model MFNP, which can rapidly adapt to users with few check-in records. Towards the cold-start problem, it seamlessly integrates carefully designed user-specific and region-specific tasks in meta-learning, such that region-aware user preferences can be captured via a rational fusion of region-independent personal preferences and region-dependent crowd preferences. In modelling region-dependent crowd preferences, a cluster-based adaptive network is adopted to capture shared preferences from similar users for knowledge transfer. Experimental results on two real-world datasets show that our model outperforms the state-of-the-art methods on next POI recommendation for cold-start users.

scholarly journals Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 214-221 ◽  
Author(s):  
Ke Sun ◽  
Tieyun Qian ◽  
Tong Chen ◽  
Yile Liang ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
State Of The Art ◽  
User Preferences ◽  
Short Term ◽  
Preference Modeling ◽  
Point Of Interest ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Novel Method ◽  
Real World Datasets

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

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

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.

scholarly journals An interpretable framework for investigating the neighborhood effect in POI recommendation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255685
Author(s):  
Guangchao Yuan ◽  
Munindar P. Singh ◽  
Pradeep K. Murukannaiah
Keyword(s):  
Deep Learning ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Experimental Results ◽  
Neighborhood Effect ◽  
Data Set ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Geographical Characteristics

Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is the neighborhood effect, which captures a user’s POI visiting behavior based on the user’s preference not only to a POI, but also to the POI’s neighborhood. To provide an interpretable framework to fully study the neighborhood effect, first, we develop different sets of insightful features, representing different aspects of neighborhood effect. We employ a Yelp data set to evaluate how different aspects of the neighborhood effect affect a user’s POI visiting behavior. Second, we propose a deep learning–based recommendation framework that exploits the neighborhood effect. Experimental results show that our approach is more effective than two state-of-the-art matrix factorization–based POI recommendation techniques.

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.

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