Open AccessProceedings Article
Fused matrix factorization with geographical and social influence in location-based social networks
Chen Cheng,Haiqin Yang,Irwin King,Michael R. Lyu +3 more
- Vol. 26, Iss: 1, pp 17-23
Reads0
Chats0
TLDR
This paper is the first to fuse MF with geographical and social influence for POI recommendation in LBSNs via modeling the probability of a user's check-in on a location as a Multicenter Gaussian Model (MGM) and fuse the geographical influence into a generalized matrix factorization framework.Abstract:
Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc, have attracted millions of users to share their social friendship and their locations via check-ins The available check-in information makes it possible to mine users' preference on locations and to provide favorite recommendations Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services To solve this task, matrix factorization is a promising tool due to its success in recommender systems However, previously proposed matrix factorization (MF) methods do not explore geographical influence, eg, multi-center check-in property, which yields suboptimal solutions for the recommendation In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs We first capture the geographical influence via modeling the probability of a user's check-in on a location as a Multicenter Gaussian Model (MGM) Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework Our solution to POI recommendation is efficient and scales linearly with the number of observations Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantlyread more
Citations
More filters
An Empirical Study of Combining Participatory and Physical Sensing to Better Understand and Improve Urban Mobility Networks
Xiao-Feng Xie,Zun-Jing Wang +1 more
TL;DR: A study combining participatory and physical sensing data, based on 3.4 million checkins collected in the Pittsburgh metropolitan area, and 125 million vehicle records collected in a sub-area controlled by an adaptive urban traffic control system is presented.
Journal ArticleDOI
Photo2Trip: Exploiting Visual Contents in Geo-Tagged Photos for Personalized Tour Recommendation
TL;DR: This paper proposes a Visual-enhanced Probabilistic Matrix Factorization model (VPMF), which integrates visual features into the collaborative filtering model, to learn user interests by leveraging the historical travel records and extends VPMF to End-to-End training framework to incorporate users (POIs) latent factors into the learning process of the visual content of photos.
Journal ArticleDOI
Leveraging graph neural networks for point-of-interest recommendations
TL;DR: This work proposes GNN-POI, a generic POI recommendation framework that leverages Graph Neural Networks (GNNs), which demonstrate powerful modeling capacity to learn node representations from node information and topological structure to improvePOI recommendation.
Journal ArticleDOI
Attentional Memory Network with Correlation-based Embedding for time-aware POI recommendation
TL;DR: Zhang et al. as discussed by the authors proposed a novel attentional memory network with correlation-based embedding (AMN-CE) for time-aware POI recommendation, which is able to capture the micro-level relationship between time slot pairs.
Posted Content
Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling
Jing Yang,Carsten Eickhoff +1 more
TL;DR: In this paper, the authors present an embedding model based on feed-forward neural networks which transforms social media check-ins into dense feature vectors encoding geographic, temporal, and functional aspects for modelling places, neighborhoods, and users.
References
More filters
Proceedings Article
Probabilistic Matrix Factorization
Andriy Mnih,Ruslan Salakhutdinov +1 more
TL;DR: The Probabilistic Matrix Factorization (PMF) model is presented, which scales linearly with the number of observations and performs well on the large, sparse, and very imbalanced Netflix dataset and is extended to include an adaptive prior on the model parameters.
Proceedings ArticleDOI
Friendship and mobility: user movement in location-based social networks
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.
Proceedings ArticleDOI
Mining interesting locations and travel sequences from GPS trajectories
TL;DR: This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.
Proceedings ArticleDOI
Collaborative filtering with temporal dynamics
TL;DR: Two leading collaborative filtering recommendation approaches are revamp and a more sensitive approach is required, which can make better distinctions between transient effects and long term patterns.
Proceedings ArticleDOI
Recommender systems with social regularization
TL;DR: This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods.