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.

scholarly journals Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kunni Han
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Data Preprocessing ◽  
Recall Rate ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user’s interest points according to the user’s behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error.

Collaborative Filtering Recommendation Algorithm Integrated into Co-Rating Impact Factor

2014 ◽  
Vol 926-930 ◽  
pp. 3004-3007
Author(s):  
Xu Yang Wang ◽  
Heng Liu
Keyword(s):  
Impact Factor ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Recommendation Accuracy ◽  
The Impact ◽  
Rating Information ◽  
Original Rating

The sparsity rating data is one of the main challenges of recommendation system. For this problem, we presented a collaborative filtering recommendation algorithm integrated into co-ratings impact factor. The method reduced the sparsity of rating matrix by filling the original rating matrix. It made the full use of rating information and took the impact on similarity of co-ratings between users into consideration when looking for the nearest neighbor so that the similarities were accurately computed. Experimental results showed that the proposed algorithm, to some extent, improved the recommendation accuracy.

scholarly journals UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 122-130
Author(s):  
Yue Huang ◽  
Xuedong Gao ◽  
Shujuan Gu
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Accurate Prediction ◽  
Similarity Measurement ◽  
Data Set ◽  
Rating Data ◽  
User Similarity ◽  
Level Difference

Abstract User similarity measurement plays a key role in collaborative filtering recommendation which is the most widely applied technique in recommender systems. Traditional user-based collaborative filtering recommendation methods focus on absolute rating difference of common rated items while neglecting the relative rating level difference to the same items. In order to overcome this drawback, we propose a novel user similarity measure which takes into account the degree of rating the level gap that users could accept. The results of collaborative filtering recommendation based on User Acceptable Rating Radius (UARR) on a real movie rating data set, the MovieLens data set, prove to generate more accurate prediction results compared to the traditional similarity methods.

An Overview of Collaborative Filtering Recommendation Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3899-3903
Author(s):  
Ping Sun ◽  
Zheng Yu Li ◽  
Zi Yang Han ◽  
Feng Ying Wang
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Evaluation Method ◽  
Decisive Role ◽  
Classical Solutions ◽  
Critical Problem ◽  
Recommendation Algorithm ◽  
Data Sparseness ◽  
Collaborative Filtering Algorithm ◽  
And Performance

Recommendation algorithm is the most core and key point in recommender systems, and plays a decisive role in type and performance evaluation. At present collaborative filtering recommendation not only is the most widely useful and successful recommend technology, but also is a promotion for the study of the whole recommender systems. The research on the recommender systems is coming into a focus and critical problem at home and abroad. Firstly, the latest development and research in the collaborative filtering recommendation algorithm are introduced. Secondly, the primary idea and difficulties faced with the algorithm are explained in detail. Some classical solutions are used to deal with the problems such as data sparseness, cold start and augmentability. Thirdly, the particular evaluation method of the algorithm is put forward and the developments of collaborative filtering algorithm are prospected.

scholarly journals Analysis of Brand Visual Design Based on Collaborative Filtering Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Gao Chaomeng ◽  
Wang Yonggang
Keyword(s):  
Collaborative Filtering ◽  
Visual Communication ◽  
Visual Design ◽  
Design System ◽  
Original Algorithm ◽  
Filtering Algorithm ◽  
Visual Communication Design ◽  
Communication Design ◽  
Collaborative Filtering Algorithm ◽  
The Impact

With the continuous development of China’s social economy, the competitiveness of brand market is gradually increasing. In order to improve their own level in brand building, major enterprises gradually explore and study visual communication design. Brand visual design has also received more and more attention. Building a complete and rich visual design system can improve the brand level and attract users to consume. Based on the abovementioned situation, this paper proposes to use collaborative filtering algorithm to analyze and study brand visual design. Firstly, a solution is proposed to solve the problem of low accuracy of general recommendation algorithm in brand goods. Collaborative filtering algorithm is used to analyze the visual communication design process of enterprise brand. Research on personalized image design according to consumers’ trust and recognition of brand design is conducted. In traditional craft brand visual design, we mainly study the impact of image design on consumer behavior. The brand loyalty model is used to predict and analyze the visual design effect. Also, the user’s evaluation coefficient is taken as the expression of brand visual design recognition. Finally, the collaborative filtering algorithm is optimized to improve the consumer similarity based on the original algorithm. The results show that the brand visual design using collaborative filtering algorithm can help enterprises obtain greater benefits in their own brand construction. It provides effective data help in the development of traditional craft brands.

Time-Weighted Collaborative Filtering Algorithm Based on Improved Mini Batch K-Means Clustering

2021 ◽  
Vol 105 ◽  
pp. 309-317
Author(s):  
Xue Han ◽  
Zhong Wang ◽  
Hui Jun Xu
Keyword(s):  
Collaborative Filtering ◽  
Sparse Matrix ◽  
Pearson Correlation ◽  
Absolute Error ◽  
User Interest ◽  
User Similarity ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Low Efficiency ◽  
Score Matrix

The traditional collaborative filtering recommendation algorithm has the defects of sparse score matrix, weak scalability and user interest deviation, which lead to the low efficiency of algorithm and low accuracy of score prediction. Aiming at the above problems, this paper proposed a time-weighted collaborative filtering algorithm based on improved Mini Batch K-Means clustering. Firstly, the algorithm selected the Pearson correlation coefficient to improve the Mini Batch K-Means clustering, and used the improved Mini Batch K-Means algorithm to cluster the sparse scoring matrix, calculated the user interest score to complete the filling of the sparse matrix. Then, considering the influence of user interest drift with time, the algorithm introduced the Newton cooling time-weighted to improve user similarity. And then calculated user similarity based on the filled score matrix, which helped to get the last predicted score of unrated items The experimental results show that, compared with the traditional collaborative filtering algorithms, the mean absolute error of Proposed improved algorithm is d, and the Precision, Recall and F1 value of MBKT-CF also get a large improvement, which has a higher rating prediction accuracy.

scholarly journals User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 121 ◽  
Author(s):  
Wenchuan Shi ◽  
Liejun Wang ◽  
Jiwei Qin
Keyword(s):  
Collaborative Filtering ◽  
Absolute Error ◽  
Rating Data ◽  
Latent Features ◽  
Collaborative Filtering Algorithm ◽  
Rating Prediction ◽  
Value Decomposition ◽  
Linear Interactions ◽  
Information Values ◽  
Explicit Feedback

The collaborative filtering algorithm based on the singular value decomposition plus plus (SVD++) model employs the linear interactions between the latent features of users and items to predict the rating in the recommendation systems. Aiming to further enrich the user model with explicit feedback, this paper proposes a user embedding model for rating prediction in SVD++-based collaborative filtering, named UE-SVD++. We exploit the user potential explicit feedback from the rating data and construct the user embedding matrix by the proposed user-wise mutual information values. In addition, the user embedding matrix is added to the existing user bias and implicit parameters in the SVD++ to increase the accuracy of the user modeling. Through extensive studies on four different datasets, we found that the rating prediction performance of the UE-SVD++ model is improved compared with other models, and the proposed model’s evaluation indicators root-mean-square error (RMSE) and mean absolute error (MAE) are decreased by 1.002–2.110% and 1.182–1.742%, respectively.

Robust Analysis on a Privacy Preserving Recommendation Algorithm under the KNN Attack

2014 ◽  
Vol 610 ◽  
pp. 717-721 ◽  
Author(s):  
Yan Gao ◽  
Jing Bo Xia ◽  
Jing Jing Ji ◽  
Ling Ma
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Privacy Preserving ◽  
Perturbation Techniques ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Robust Analysis ◽  
History Of

— Among algorithms in recommendation system, Collaborative Filtering (CF) is a popular one. However, the CF methods can’t guarantee the safety of the user rating data which cause private preserving issue. In general, there are four kinds of methods to solve private preserving: Perturbation, randomization, swapping and encryption. In this paper, we mimic algorithms which attack the privacy-preserving methods with randomized perturbation techniques. After leaking part of rating history of a customer, we can infer this customer’s other rating history. At the end, we propose an algorithm to enhance the system so as to avoid being attacked.

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