Research on Collaborative Filtering Recommendation Algorithm for Improving User Similarity Calculation

2021 ◽  
Author(s):  
Gong Lixia ◽  
Wang Junyi
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Algorithm ◽  
User Similarity ◽  
Similarity Calculation

scholarly journals Probabilistic Matrix Factorization Recommendation Algorithm with User Trust Similarity

2018 ◽  
Vol 208 ◽  
pp. 05004
Author(s):  
Yuxin Dong ◽  
Shuyun Fang ◽  
Kai Jiang ◽  
Fukun Chen ◽  
Guisheng Yin
Keyword(s):  
Conference Proceedings ◽  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Cold Start ◽  
Recommendation Algorithm ◽  
User Similarity ◽  
Similarity Calculation ◽  
Probabilistic Matrix Factorization ◽  
User Trust

In this paper, we describe the formatting guidelines for Conference Proceedings. Whether the user similarity calculation is reasonable in the traditional collaborative filtering recommendation algorithm directly affects the result of the collaborative filtering recommendation algorithm. This paper proposes a probabilistic matrix factorization recommendation algorithm with user trust similarity which combines improved similarity of users’ trust and probability matrix factorization recommendation method. The results show that proposed algorithm could relieve user cold start issues and effectively reduce the error of recommendation.

scholarly journals Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

2021 ◽  
Vol 11 (20) ◽  
pp. 9554
Author(s):  
Jianjun Ni ◽  
Yu Cai ◽  
Guangyi Tang ◽  
Yingjuan Xie
Keyword(s):  
Collaborative Filtering ◽  
User Characteristics ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Rating Data ◽  
User Similarity ◽  
Public Data ◽  
Weighted Algorithm ◽  
Collaborative Filtering Algorithm ◽  
The Impact

The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.

An IPTV Recommendation Method Based on Latent Context

2014 ◽  
Vol 668-669 ◽  
pp. 1237-1242
Author(s):  
Wen Ming Guo ◽  
Liang Sun
Keyword(s):  
Collaborative Filtering ◽  
Improved Method ◽  
Network Television ◽  
Recommendation Algorithm ◽  
Time Model ◽  
Data Scarcity ◽  
Algorithm Construct ◽  
Similarity Calculation ◽  
Similar Item ◽  
The Impact

In the light of the data differences between network television and the Internet, this paper solve the problem of grading IPTV by the introduction of time context information and computing the latent scores based on the traditional and item-based collaborative filtering recommendation algorithm. Construct the user - item, the item - time model and optimize item similarity calculation so as to ease the difficulty of searching the similar item due to the data scarcity. The experimental results show that the improved method can obviously increase the recommendation precision and has a certain effect on reducing the impact of data scarcity compared with the traditional item-based collaborative filtering.

scholarly journals Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xiushan Zhang
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Absolute Error ◽  
User Model ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

scholarly journals A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guangxia Xu ◽  
Zhijing Tang ◽  
Chuang Ma ◽  
Yanbing Liu ◽  
Mahmoud Daneshmand
Keyword(s):  
Collaborative Filtering ◽  
Traditional Method ◽  
Recommendation System ◽  
Rapid Development ◽  
Poor Performance ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Similarity Calculation

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

scholarly journals Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuan Fang ◽  
Jingning Li
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Online Teaching ◽  
Calculation Model ◽  
Cosine Similarity ◽  
Personalized Recommendation ◽  
Functional Modules ◽  
Recommendation Algorithm ◽  
Teaching System ◽  
Similarity Calculation

This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.

scholarly journals Collaborative filtering recommendation algorithm integrating user similarity and trust

2021 ◽  
Vol 2010 (1) ◽  
pp. 012028
Author(s):  
Mengge Huang ◽  
Kai Cao ◽  
Jingyi Zhang ◽  
Chuanlin Zhang ◽  
Tao Deng
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Algorithm ◽  
User Similarity

scholarly journals A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hailong Chen ◽  
Haijiao Sun ◽  
Miao Cheng ◽  
Wuyue Yan
Keyword(s):  
Collaborative Filtering ◽  
Personalized Recommendation ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Weighting Method ◽  
Trust Value ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.

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