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 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.

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 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 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.

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

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
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.

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.

scholarly journals Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach

2017 ◽  
Author(s):  
Huayu Li ◽  
Yong Ge ◽  
Defu Lian ◽  
Hao Liu
Keyword(s):  
Missing Data ◽  
General Interest ◽  
Complex Nature ◽  
Unified Approach ◽  
Fine Grained ◽  
Data Sparseness ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Real World Datasets ◽  
Location Based Social Networks

Point-of-Interest (POI) recommendation has been an important service on location-based social networks. However, it is very challenging to generate accurate recommendations due to the complex nature of user's interest in POI and the data sparseness. In this paper, we propose a novel unified approach that could effectively learn fine-grained and interpretable user's interest, and adaptively model the missing data. Specifically, a user's general interest in POI is modeled as a mixture of her intrinsic and extrinsic interests, upon which we formulate the ranking constraints in our unified recommendation approach. Furthermore, a self-adaptive location-oriented method is proposed to capture the inherent property of missing data, which is formulated as squared error based loss in our unified optimization objective. Extensive experiments on real-world datasets demonstrate the effectiveness and advantage of our approach.

Sign in / Sign up

Export Citation Format

Share Document