A self-adaptive point-of-interest recommendation algorithm based on a multi-order Markov model

2018 ◽  
Vol 89 ◽  
pp. 506-514 ◽  
Author(s):  
Shudong Liu ◽  
Lei Wang
Keyword(s):  
Markov Model ◽  
Recommendation Algorithm ◽  
Point Of Interest ◽  
Self Adaptive

A New Self-Adaptive Hybrid Markov Topic Model Poi Recommendation in Social Networks

2021 ◽  
pp. 2250039
Author(s):  
Bin Xu ◽  
Chuanming Ge ◽  
Wei Zhao ◽  
Jianhua Cao ◽  
Ruilin Pan
Keyword(s):  
Social Networks ◽  
Markov Model ◽  
Topic Model ◽  
Generation Model ◽  
Behavioral Patterns ◽  
Point Of Interest ◽  
Single User ◽  
The Individual ◽  
Individual Self ◽  
Self Adaptive

Point-of-Interest recommendation is an efficient way to explore interesting unknown locations in social media mining of social networks. In order to solve the problem of sparse data and inaccuracy of single user model, we propose a User-City-Sequence Probabilistic Generation Model (UCSPGM) integrating a collective individual self-adaptive Markov model and the topic model. The collective individual self-adaptive Markov model consists of three parts such as the collective Markov model, the individual self-adaptive Markov model and the self-adaptive rank method. The former determines the topic sequence for all users in system and mines the behavioral patterns of users in a large environment. The later mines behavioral patterns for each user in a small environment. The last determines a self-adaptive-rank for each user in niche. We conduct a large amount of experiments to verify the effectiveness and efficiency of our method.

scholarly journals A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Li Liu ◽  
Dashi Luo ◽  
Ming Liu ◽  
Jun Zhong ◽  
Ye Wei ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Training Dataset ◽  
Support Vector ◽  
Emotion Classification ◽  
Online Social Media ◽  
Hidden Knowledge ◽  
Self Adaptive

Microblogging is increasingly becoming one of the most popular online social media for people to express ideas and emotions. The amount of socially generated content from this medium is enormous. Text mining techniques have been intensively applied to discover the hidden knowledge and emotions from this huge dataset. In this paper, we propose a modified version of hidden Markov model (HMM) classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that theF-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.

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

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.

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

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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