scholarly journals Integrating an Attention Mechanism and Convolution Collaborative Filtering for Document Context-Aware Rating Prediction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 3826-3835 ◽  
Author(s):  
Bangzuo Zhang ◽  
Haobo Zhang ◽  
Xiaoxin Sun ◽  
Guozhong Feng ◽  
Chunguang He
Keyword(s):  
Collaborative Filtering ◽  
Attention Mechanism ◽  
Context Aware ◽  
Rating Prediction

scholarly journals Extreme Residual Connected Convolution-Based Collaborative Filtering for Document Context-Aware Rating Prediction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 53604-53613 ◽  
Author(s):  
Bangzuo Zhang ◽  
Min Zhu ◽  
Mengying Yu ◽  
Dongbing Pu ◽  
Guozhong Feng
Keyword(s):  
Collaborative Filtering ◽  
Context Aware ◽  
Rating Prediction

A new approach for rating prediction system using collaborative filtering

2018 ◽  
Vol 2 (2) ◽  
pp. 81-87 ◽  
Author(s):  
Pushpendra Kumar ◽  
Vinod Kumar ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Collaborative Filtering ◽  
Prediction System ◽  
New Approach ◽  
Rating Prediction

Industrialization Research and Realization of Preprocessing in Rough Set Based on Similar Prediction

2012 ◽  
Vol 251 ◽  
pp. 185-190
Author(s):  
Dun Hong Yao ◽  
Xiao Ning Peng ◽  
Jia He
Keyword(s):  
Data Processing ◽  
Collaborative Filtering ◽  
Rough Set ◽  
Data Preprocessing ◽  
Sparse Data ◽  
Data Evaluation ◽  
Prediction Algorithm ◽  
Rating Prediction ◽  
Essential Problem ◽  
Data Processing Methods

In every field which needs data processing, the sparseness of data is an essential problem that should be resolved, especially in movies, shopping sites. The users with the same commodity preferences makes the data evaluation valuable. Otherwise, without any evaluation of information, it will result in sparse distribution of the entire data obtained. This article introduces a collaborative filtering technology used in sparse data processing methods - project-based rating prediction algorithm, and extends it to the areas of rough set, the sparse information table processing, rough set data preprocessing sparse issues.

Popularised Similarity Function for Effective Collaborative Filtering Recommendations

2020 ◽  
Vol 10 (1) ◽  
pp. 34-47
Author(s):  
Abba Almu ◽  
Abubakar Roko ◽  
Aminu Mohammed ◽  
Ibrahim Saidu
Keyword(s):  
Collaborative Filtering ◽  
Experimental Studies ◽  
Similarity Function ◽  
Similarity Functions ◽  
Rating Prediction

The existing similarity functions use the user-item rating matrix to process similar neighbours that can be used to predict ratings to the users. However, the functions highly penalise high popular items which lead to predicting items that may not be of interest to active users due to the punishment function employed. The functions also reduce the chances of selecting less popular items as similar neighbours due to the items with common ratings used. In this article, a popularised similarity function (pop_sim) is proposed to provide effective recommendations to users. The pop_sim function introduces a modified punishment function to minimise the penalty on high popular items. The function also employs a popularity constraint which uses ratings threshold to increase the chances of selecting less popular items as similar neighbours. The experimental studies indicate that the proposed pop_sim is effective in improving the accuracy of the rating prediction in terms of not only lowering the MAE but also the RMSE.

Graph-based context-aware collaborative filtering

2019 ◽  
Vol 126 ◽  
pp. 9-19 ◽  
Author(s):  
Tu Minh Phuong ◽  
Do Thi Lien ◽  
Nguyen Duy Phuong
Keyword(s):  
Collaborative Filtering ◽  
Context Aware

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.

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