G-DAN-SNR: An improved Personalized POI recommendation algorithm based on gating and attention mechanism

The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

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A Multi-factor Recommendation Algorithm for POI Recommendation

Web Information Systems and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-030-02934-0_41 ◽

2018 ◽

pp. 445-454 ◽

Cited By ~ 2

Author(s):

Rong Yang ◽

Xiaofeng Han ◽

Xingzhong Zhang

Keyword(s):

Recommendation Algorithm ◽

Poi Recommendation

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P3OI-MELSH: Privacy Protection Target Point of Interest Recommendation Algorithm Based on Multi-Exploring Locality Sensitive Hashing

Frontiers in Neurorobotics ◽

10.3389/fnbot.2021.660304 ◽

2021 ◽

Vol 15 ◽

Author(s):

Desheng Liu ◽

Linna Shan ◽

Lei Wang ◽

Shoulin Yin ◽

Hui Wang ◽

...

Keyword(s):

Social Network ◽

Privacy Protection ◽

Rapid Development ◽

Locality Sensitive Hashing ◽

Target Point ◽

Interest Points ◽

Recommendation Algorithm ◽

Original Algorithm ◽

Point Of Interest ◽

Poi Recommendation

With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by users in the era of big data, improve user experience, but also help merchants quickly find target users and achieve accurate marketing. Most of the works are based on users' check-in history and social network data to model users' personalized preferences for interest points, and recommend interest points through collaborative filtering and other recommendation technologies. However, in the check-in history, the multi-source heterogeneous information (including the position, category, popularity, social, reviews) describes user activity from different aspects which hides people's life style and personal preference. However, the above methods do not fully consider these factors' combined action. Considering the data privacy, it is difficult for individuals to share data with others with similar preferences. In this paper, we propose a privacy protection point of interest recommendation algorithm based on multi-exploring locality sensitive hashing (LSH). This algorithm studies the POI recommendation problem under distributed system. This paper introduces a multi-exploring method to improve the LSH algorithm. On the one hand, it reduces the number of hash tables to decrease the memory overhead; On the other hand, the retrieval range on each hash table is increased to reduce the time retrieval overhead. Meanwhile, the retrieval quality is similar to the original algorithm. The proposed method uses modified LSH and homomorphic encryption technology to assist POI recommendation which can ensure the accuracy, privacy and efficiency of the recommendation algorithm, and it verifies feasibility through experiments on real data sets. In terms of root mean square error (RMSE), mean absolute error (MAE) and running time, the proposed method has a competitive advantage.

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A HITS-based POI recommendation algorithm for location-based social networks

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13 ◽

10.1145/2492517.2492652 ◽

2013 ◽

Cited By ~ 10

Author(s):

Xuelian Long ◽

James Joshi

Keyword(s):

Social Networks ◽

Recommendation Algorithm ◽

Poi Recommendation ◽

Location Based Social Networks

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A graph neural network based algorithm for Point-of-Interest recommendation using social relation and time series

International Journal of Web Services Research ◽

10.4018/ijwsr.2021100103 ◽

2021 ◽

Vol 18 (4) ◽

pp. 0-0

Keyword(s):

Neural Network ◽

Social Relationships ◽

Euclidean Distance ◽

User Behavior ◽

Recommendation Algorithm ◽

Social Time ◽

Point Of Interest ◽

Poi Recommendation ◽

Location Based Social Networks ◽

Time Information

POI recommendation has gradually become an important topic in the field of service recommendation, which is always achieved by mining user behavior patterns. However, the context information of the collaborative signal is not encoded in the embedding process of traditional POI recommendation methods, which is not enough to capture the collaborative signal among different users. Therefore, a POI recommendation algorithm is presented by using social-time context graph neural network model (GNN) in Location-based social networks. First, it finds similarities between different social relationships based on the users' social and temporal behavior. Then, the similarity among different users is calculated by an improved Euclidean distance. Finally, it integrates the graph neural network, the interaction bipartite graph of users and social-time information into the algorithm to generate the final recommendation list in this paper. Experiments on real datasets show that the proposed method is superior to the state-of-the-art POI recommendation methods.

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