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.

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.

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 An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 68301-68310 ◽  
Author(s):  
Dionisis Margaris ◽  
Anna Kobusinska ◽  
Dimitris Spiliotopoulos ◽  
Costas Vassilakis
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Rating Prediction

Research of User-Based Collaborative Filtering Algorithm in Tag Recommendation

2014 ◽  
Vol 926-930 ◽  
pp. 3677-3683
Author(s):  
Qi Zhi Qiu ◽  
Jin Bao Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Tag Recommendation ◽  
Rating Data ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

Collaborative Filtering (CF) is one of the most popular techniques used in recommendation systems. User-based CF uses rating data to calculate similarity between users and is effective in traditional recommendation systems. However, it does not work well when applied in tag recommendation systems as there are tags with same semantics which does not match literally. Based on tag concept category, the improvement of traditional CF is achieved by compressing the dataset, analyzing the users preference and extracting the tags objective characteristics. The result of experiment shows the more satisfied accuracy than the traditional one does.

A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

2014 ◽  
Vol 610 ◽  
pp. 747-751
Author(s):  
Jian Sun ◽  
Xiao Ying Chen
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Cloud Model ◽  
Prediction Algorithm ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
User Data ◽  
Item Attribute ◽  
Rating Prediction ◽  
User Ratings

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

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 Coating matching recommendation based on improved fuzzy comprehensive evaluation and collaborative filtering algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuan Xin ◽  
Bu Henan ◽  
Niu Jianmin ◽  
Yu Wenjuan ◽  
Zhou Honggen ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Absolute Error ◽  
Evaluation Index System ◽  
Scientific Management ◽  
Coating Process ◽  
Process Data ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

AbstractCoating matching design is one of the important parts of ship coating process design. The selection of coating matching is influenced by various factors such as marine corrosive environment, anti-corrosion period and working conditions. There are also differences in the coating performance requirements for different ship types and different coating parts. At present, the design of coating matching in shipyards depends on the experience of technologist, which is not conducive to the scientific management of ship painting process and the macro control of ship construction cost. Therefore, this paper proposes a hybrid algorithm of fuzzy comprehensive evaluation and collaborative filtering based on user label improvement (IFCE-CF). Based on the analytic hierarchy process (AHP), the evaluation index system of coating matching is constructed, and the weight calculation process of fuzzy comprehensive evaluation is optimized by introducing the user label weight. The collaborative filtering algorithm based on matrix decomposition is used to realize the accurate recommendation of coating matching. Historical coating process data of a shipyard between 2010 and 2020 are selected to verify the recommendation ability of the method in the paper. The results show that using the coating matching intelligent recommendation algorithm proposed in this paper, the root mean square error is < 1.02 and the mean absolute error is < 0.75, the prediction accuracy is significantly better than other research methods, which proves the effectiveness of the method.

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