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

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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Discovering Subsequence Patterns for Next POI Recommendation

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

10.24963/ijcai.2020/445 ◽

2020 ◽

Cited By ~ 3

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.

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

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/415 ◽

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.

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

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5353 ◽

2020 ◽

Vol 34 (01) ◽

pp. 214-221 ◽

Cited By ~ 3

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.

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Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/206 ◽

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.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8080355 ◽

2019 ◽

Vol 8 (8) ◽

pp. 355 ◽

Cited By ~ 5

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.

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

Mathematical Problems in Engineering ◽

10.1155/2020/6582676 ◽

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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An interpretable framework for investigating the neighborhood effect in POI recommendation

PLoS ONE ◽

10.1371/journal.pone.0255685 ◽

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.

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

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

10.24963/ijcai.2018/539 ◽

2018 ◽

Cited By ~ 34

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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Contrastive Trajectory Learning for Tour Recommendation

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3462331 ◽

2022 ◽

Vol 13 (1) ◽

pp. 1-25

Author(s):

Fan Zhou ◽

Pengyu Wang ◽

Xovee Xu ◽

Wenxin Tai ◽

Goce Trajcevski

Keyword(s):

Data Augmentation ◽

State Of The Art ◽

Implicit Feedback ◽

Data Sparsity ◽

Weak Supervision ◽

Point Of Interest ◽

Data Correlations ◽

Recommendation Accuracy ◽

Real World Datasets ◽

Trajectory Learning

The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions planned to visit, and so on. Previous PTR solutions are based on either heuristics for solving the orienteering problem to maximize a global reward with a specified budget or approaches attempting to learn user visiting preferences and transition patterns with the stochastic process or recurrent neural networks. However, existing learning methodologies rely on historical trips to train the model and use the next visited POI as the supervised signal, which may not fully capture the coherence of preferences and thus recommend similar trips to different users, primarily due to the data sparsity problem and long-tailed distribution of POI popularity. This work presents a novel tour recommendation model by distilling knowledge and supervision signals from the trips in a self-supervised manner. We propose Contrastive Trajectory Learning for Tour Recommendation (CTLTR), which utilizes the intrinsic POI dependencies and traveling intent to discover extra knowledge and augments the sparse data via pre-training auxiliary self-supervised objectives. CTLTR provides a principled way to characterize the inherent data correlations while tackling the implicit feedback and weak supervision problems by learning robust representations applicable for tour planning. We introduce a hierarchical recurrent encoder-decoder to identify tourists’ intentions and use the contrastive loss to discover subsequence semantics and their sequential patterns through maximizing the mutual information. Additionally, we observe that a data augmentation step as the preliminary of contrastive learning can solve the overfitting issue resulting from data sparsity. We conduct extensive experiments on a range of real-world datasets and demonstrate that our model can significantly improve the recommendation performance over the state-of-the-art baselines in terms of both recommendation accuracy and visiting orders.

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Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks

Complexity ◽

10.1155/2019/3574194 ◽

2019 ◽

Vol 2019 ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

Lei Guo ◽

Haoran Jiang ◽

Xiyu Liu ◽

Changming Xing

Keyword(s):

Neural Network ◽

Collaborative Filtering ◽

Training Data ◽

Geographical Information ◽

Network Information ◽

Point Of Interest ◽

Poi Recommendation ◽

Real World Datasets ◽

Location Based Social Networks ◽

High Level

As one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years. Compared with traditional recommendation problems, POI recommendations are suffering from more challenges, such as the cold-start and one-class collaborative filtering problems. Many existing studies have focused on how to overcome these challenges by exploiting different types of contexts (e.g., social and geographical information). However, most of these methods only model these contexts as regularization terms, and the deep information hidden in the network structure has not been fully exploited. On the other hand, neural network-based embedding methods have shown its power in many recommendation tasks with its ability to extract high-level representations from raw data. According to the above observations, to well utilize the network information, a neural network-based embedding method (node2vec) is first exploited to learn the user and POI representations from a social network and a predefined location network, respectively. To deal with the implicit feedback, a pair-wise ranking-based method is then introduced. Finally, by regarding the pretrained network representations as the priors of the latent feature factors, an embedding-based POI recommendation method is proposed. As this method consists of an embedding model and a collaborative filtering model, when the training data are absent, the predictions will mainly be generated by the extracted embeddings. In other cases, this method will learn the user and POI factors from these two components. Experiments on two real-world datasets demonstrate the importance of the network embeddings and the effectiveness of our proposed method.

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