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
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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
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Efficient Point-of-Interest Recommendation Services With Heterogenous Hypergraph Embedding
TL;DR: Zhang et al. as mentioned in this paper proposed a heterogeneous hypergraph embedding method for POI recommendation in LBSNs with three original contributions: first, they model the LBSN as a hypergraph to capture the complex interactions and learn the hypergraph by preserving homophily and interaction attribute affinity.
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Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms
TL;DR: In this article, the authors consider the location privacy-preserving mechanism with the assumption that the adversary has a perfect statistical model for the user location and show that under practical assumptions on the adversary's knowledge, the remapping technique leaks privacy not only about the true location data but also about the statistical model.
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Influences of Deep Learning on Recommendation Systems
TL;DR: A comprehensive survey and comparative analysis of the state-of-art research techniques based extensively on recommendation systems used for applications related to sequence learning are provided.
A proximity-based recommender system for indoor spaces
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BookDOI
Information retrieval technology: 15th Asia Information Retrieval Societies Conference, AIRS 2019, Hong Kong, China, November 7–9, 2019, Proceedings
Fu Lee Wang,Haoran Xie,Wai Lam,Aixin Sun,Lun-Wei Ku,Tianyong Hao,Wei Chen,Tak-Lam Wong,Xiaohui Tao +8 more
TL;DR: This book constitutes the refereed proceedings of the 15th Information Retrieval Technology Conference, AIRS 2019, held in Hong Kong, China, in November 2019.
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