scholarly journals Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Zhen Liu ◽  
Huanyu Meng ◽  
Shuang Ren ◽  
Feng Liu
Keyword(s):  
Collaborative Filtering ◽  
Clustering Algorithm ◽  
Behavior Pattern ◽  
Location Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Location Based Social Network ◽  
Spatio Temporal ◽  
Physical Clusters ◽  
Message Propagation ◽  
Core Issues

Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN). When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately.

A learning resource recommendation algorithm based on online learning sequential behavior

2019 ◽  
Vol 17 (02) ◽  
pp. 1940001 ◽  
Author(s):  
Xuebin Wang ◽  
Zhengzhou Zhu ◽  
Jiaqi Yu ◽  
Ruofei Zhu ◽  
DeQi Li ◽  
...  
Keyword(s):  
Online Learning ◽  
Collaborative Filtering ◽  
Clustering Algorithm ◽  
Recall Rate ◽  
Learning Resources ◽  
Recommendation Algorithm ◽  
Sequential Behavior ◽  
Learning Resource ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

The accuracy of learning resource recommendation is crucial to realizing precise teaching and personalized learning. We propose a novel collaborative filtering recommendation algorithm based on the student’s online learning sequential behavior to improve the accuracy of learning resources recommendation. First, we extract the student’s learning events from his/her online learning process. Then each student’s learning events are selected as the basic analysis unit to extract the feature sequential behavior sequence that represents the student’s learning behavioral characteristics. Then the extracted feature sequential behavior sequence generates the student’s feature vector. Moreover, we improve the H-[Formula: see text] clustering algorithm that clusters the students who have similar learning behavior. Finally, we recommend learning resources to the students combine similarity user clusters with the traditional collaborative filtering algorithm based on user. The experiment shows that the proposed algorithm improved the accuracy rate by 110% and recall rate by 40% compared with the traditional user-based collaborative filtering algorithm.

scholarly journals Recommending Locations Based on Users’ Periodic Behaviors

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Bing Xu ◽  
Zhijun Ding ◽  
Hongzhong Chen
Keyword(s):  
Time Series ◽  
Social Network ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
New Model ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Location Based Social Network ◽  
Gps Trajectory ◽  
Life Behavior

The research of location recommendation system is an important topic in the field of LBSN (Location-Based Social Network). Recently, more and more researchers began focusing on researching how to recommend locations based on user’s life behavior. In this paper, we proposed a new model recommending locations based on user’s periodic behaviors. In view of multiple periodic behaviors existing in time series, an algorithm which can mine all periods in time series is proposed in this paper. Based on the periodic behaviors, we recommend locations using item-based collaborative filtering algorithm. In this paper, we will also introduce our recommendation system which can collect users’ GPS trajectory, mine user’s multiple periods, and recommend locations based user’s periodic behavior.

scholarly journals Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xibin Wang ◽  
Zhenyu Dai ◽  
Hui Li ◽  
Jianfeng Yang
Keyword(s):  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Time Effect ◽  
Time Factor ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Neighbor Selection ◽  
Hybrid Collaborative Filtering

In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.

Research and implementation of e-commerce intelligent recommendation system based on fuzzy clustering algorithm

2021 ◽  
pp. 1-10
Author(s):  
Jinjuan Hu ◽  
Chao Xie
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Rapid Development ◽  
Filtering Algorithm ◽  
Fuzzy Clustering Algorithm ◽  
Analysis Experiment ◽  
Collaborative Filtering Algorithm

After entering the 21st century, the electronic commerce system has affected all aspects of our lives. Whether we read news on our mobile phones or computers or purchase items on our online websites, it greatly facilitates our lives. With the rapid development of short videos, many people like to watch small videos that interest them. The rapid development of e-commerce has facilitated our lives, so that we no longer have to go to many shopping malls to buy our favorite items, and we also no need to change TV stations one by one after watching a program to find our favorite programs. However, due to the rapid development of electronic commerce, there has been a lot of information overload. When users browse the website, items they are not interested in will appear, and even information about online fraud appears. How to filter this information and how to intelligently recommend to users more favorite items is the main research direction of this article. The research of this article is mainly divided into four parts. The first part analyzes the current situation of intelligent recommendation technology research and puts forward the idea of this article. The second part introduces the commonly used collaborative filtering algorithm and the principle and process of the fuzzy clustering algorithm used in this experiment, analyzes the shortcomings of the traditional collaborative filtering algorithm and illustrates the adaptability of the fuzzy clustering algorithm in practical applications. The third part introduces an intelligent recommendation system based on fuzzy clustering, which comprehensively analyzes the characteristics of users and products, makes full use of users’ evaluation information of products, and realizes intelligent recommendations based on content and collaborative filtering. At the end of the article, the comparative analysis experiment with the intelligent recommendation system of collaborative recommendation algorithm further proves the superiority of the intelligent recommendation system of electronic commerce based on fuzzy clustering algorithm in this paper and improves the accuracy of intelligent recommendation.

scholarly journals A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

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