Spatio-Temporal Representation Learning with Social Tie for Personalized POI Recommendation (original) (raw)
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Learning Graph-based POI Embedding for Location-based Recommendation
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016
With the rapid prevalence of smart mobile devices and the dramatic proliferation of location-based social networks (LB-SNs), location-based recommendation has become an important means to help people discover attractive and interesting points of interest (POIs). However, extreme sparsity of user-POI matrix and cold-start issue create severe challenges, causing CF-based methods to degrade significantly in their recommendation performance. Moreover, locationbased recommendation requires spatiotemporal context awareness and dynamic tracking of the user's latest preferences in a real-time manner. To address these challenges, we stand on recent advances in embedding learning techniques and propose a generic graphbased embedding model, called GE, in this paper. GE jointly captures the sequential effect, geographical influence, temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs (POI-POI, POI-Region, POI-Time and POI-Word) into a shared low dimensional space. Then, to support real-time recommendation, we develop a novel time-decay method to dynamically compute the user's latest preferences based on the embedding of his/her checked-in POIs learnt in the latent space. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets, and the experimental results show its superiority over other competitors, especially in recommending cold-start POIs. Besides, we study the contribution of each factor to improve location-based recommendation, and find that both sequential effect and temporal cyclic effect play more important roles than geographical influence and semantic effect.
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020
Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in a local view based on independent user visit sequences. This limits the model's ability to directly connect and learn across users in a global view to recommend semantically trained POIs. In this work, we propose a Spatial-Temporal-Preference User Dimensional Graph Attention Network (STP-UDGAT), a novel explore-exploit model that concurrently exploits personalized user preferences and explores new POIs in global spatial-temporal-preference (STP) neighbourhoods, while allowing users to selectively learn from other users. In addition, we propose random walks as a masked self-attention option to leverage the STP graphs' structures and find new higher-order POI neighbours during exploration. Experimental results on six real-world datasets show that our model significantly outperforms baseline and state-of-the-art methods. CCS CONCEPTS • Information systems → Recommender systems.
STAN: Spatio-Temporal Attention Network for Next Location Recommendation
Proceedings of the Web Conference 2021, 2021
The next location recommendation is at the core of various locationbased applications. Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical gridding and model temporal relation with explicit time intervals, while some vital questions remain unsolved. Non-adjacent locations and non-consecutive visits provide non-trivial correlations for understanding a user's behavior but were rarely considered. To aggregate all relevant visits from user trajectory and recall the most plausible candidates from weighted representations, here we propose a Spatio-Temporal Attention Network (STAN) for location recommendation. STAN explicitly exploits relative spatiotemporal information of all the checkins with self-attention layers along the trajectory. This improvement allows a point-to-point interaction between non-adjacent locations and non-consecutive check-ins with explicit spatio-temporal effect. STAN uses a bi-layer attention architecture that firstly aggregates spatiotemporal correlation within user trajectory and then recalls the target with consideration of personalized item frequency (PIF). By visualization, we show that STAN is in line with the above intuition. Experimental results unequivocally show that our model outperforms the existing state-of-the-art methods by 9-17%. CCS CONCEPTS • Information systems → Location based services; Data mining; • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods.
Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Learning which Point-of-Interest (POI) a user will visit next is a challenging task for personalized recommender systems due to the large search space of possible POIs in the region. A recurring problem among existing works that makes it difficult to learn and perform well is the sparsity of the User-POI matrix. In this paper, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the data sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we also propose a novel selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our model significantly outperforms baseline and the state-of-the-art methods. CCS CONCEPTS • Information systems → Recommender systems.
Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation
IEEE Transactions on Knowledge and Data Engineering, 2017
Point-of-interest (POI) recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. However, the extreme sparsity of user-POI matrix and cold-start issues severely hinder the performance of collaborative filtering-based methods. Moreover, user preferences may vary dramatically with respect to the geographical regions due to different urban compositions and cultures. To address these challenges, we stand on recent advances in deep learning and propose a Spatial-Aware Hierarchical Collaborative Deep Learning model (SH-CDL). The model jointly performs deep representation learning for POIs from heterogeneous features and hierarchically additive representation learning for spatial-aware personal preferences. To combat data sparsity in spatial-aware user preference modeling, both the collective preferences of the public in a given target region and the personal preferences of the user in adjacent regions are exploited in the form of social regularization and spatial smoothing. To deal with the multimodal heterogeneous features of the POIs, we introduce a late feature fusion strategy into our SH-CDL model. The extensive experimental analysis shows that our proposed model outperforms the state-of-the-art recommendation models, especially in out-of-town and cold-start recommendation scenarios.
Graph-Flashback Network for Next Location Recommendation
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Next Point-of Interest (POI) recommendation plays an important role in location-based applications, which aims to recommend the next POIs to users that they are most likely to visit based on their historical trajectories. Existing methods usually use rich side information, or customized POI graphs to capture the sequential patterns among POIs. However, the graphs only focus on connectivity between POIs. Few studies propose to explicitly learn a weighted POI graph, which could reflect the transition patterns among POIs and show the importance of its different neighbors for each POI. In addition, these approaches simply utilize the user characteristics for personalized POI recommendation without sufficient consideration. To this end, we construct a novel User-POI Knowledge Graph with strong representation ability, called Spatial-Temporal Knowledge Graph (STKG). STKG is used to learn the representations of each node (i.e., user, POI) and each edge. Then, we design a similarity function to construct our POI transition graph based on the learned representations. To incorporate the learned graph into sequential model, we propose a novel network Graph-Flashback for recommendation. Graph-Flashback applies a simplified Graph Convolution Network (GCN) on the POI transition graph to enrich the representation of each POI. Further, we define a similarity function to consider both spatiotemporal information and user preference in modelling sequential regularity. Experimental results on two real-world datasets show that our proposed method achieves the state-of-the-art performance and significantly outperforms all existing solutions.
Location Embeddings for Next Trip Recommendation
2019
The amount of information available in social media and specialized blogs has become useful for a user to plan a trip. However, the user is quickly overwhelmed by the list of possibilities offered to him, making his search complex and time-consuming. Recommender systems aim to provide personalized suggestions to users by leveraging different type of information, thus assisting them in their decision-making process. Recently, the use of neural networks and knowledge graphs have proven to be efficient for items recommendation. In our work, we propose an approach that leverages contextual, collaborative and content information in order to recommend personalized destinations to travelers. We compare our approach with a set of state of the art collaborative filtering methods and deep learning based recommender systems.
Clustering-based Location Authority Deep Model in the Next Point-of-Interest Recommendation
IEEE/WIC/ACM International Conference on Web Intelligence, 2021
With the development of location-based social networks (LBSNs), sequential Point-of-Interest (POI) recommendation is getting more and more vitality. Current sequential POI recommendation models predict user's future mobility based on user's previous behaviors without considering the effect of the significant location (i.e. location authority). Besides, it is hard to effectively capture user interests because of the data sparsity problem in the LBSNs dataset. To this end, we propose a clustering-based location authority deep model (CLADM) that integrates POI clusters and location authority to reduce the scale of POI candidate set to alleviate the issue of data sparsity. In the proposed model, we present two encoders based on the attention mechanism: the first encoder is an additive attention encoder to exploit user preferences on POI clusters, and the second encoder mines user preferences of POIs. Considering that the user check-in data is the implicit feedback, we design a binary self-attention layer in the second encoder with a sigmoid function. We adopt two real-world LBSNs datasets with different scales to evaluate our model. The experimental results show that our proposed model greatly outperforms the state-of-the-art methods for sequential POI recommendation.
2018
Point-of-interest recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. User performance may very important with respect to the geographical reason with different urban area cultures, environment. For solving these all issue I have to propose deep learning and proposed a special aware hierarchical collaborative deep learning algorithm. In this model jointly perform deep representation learning for point of interest from heterogeneous feature and hierarchical additive information learning from special aware individual performance. the collective preferences of the public in a given target regionand the personal preferences of the user in adjacent regions are exploited in the form of social regularization and spatial smoothing. To deal with the multimodal heterogeneous features of the point of interests, we introduce a late feature fusion strategy into our special aware hierarchical collaborative ...
Personalized Ranking Metric Embedding for Next New POI Recommendation
2015
The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next...