Novel SDDM rating prediction models for recommendation systems
The accuracy of behavioral interactive features is a key factor for improving the performance
of rating prediction. In order to deeply explore the potential rules of user behavior and …
of rating prediction. In order to deeply explore the potential rules of user behavior and …
Dual Auto-Encoder Based Rating Prediction Recommendation Algorithm
G Xin, J Qin, X Song, J Zheng - IEEE Access, 2022 - ieeexplore.ieee.org
Collaborative filtering is the most widely used method in recommendation algorithms, but it
still faces the serious problem of data sparsity. Traditional collaborative filtering uses matrix …
still faces the serious problem of data sparsity. Traditional collaborative filtering uses matrix …
RP-LGMC: Rating prediction based on local and global information with matrix clustering
W Zhang, Q Wang, T Yoshida, J Li - Computers & Operations Research, 2021 - Elsevier
Recommendation system has attracted large amount of attention in the field of E-commerce
research. Traditional MF (Matrix Factorization) methods take a global view on the user-item …
research. Traditional MF (Matrix Factorization) methods take a global view on the user-item …
A novel learning model based on trust diffusion and global item for recommender systems
Y Li, J Liu, J Ren - IEEE Access, 2019 - ieeexplore.ieee.org
Recommender systems can provide users with an ordered list of various items, which greatly
assists users to purchase products that they are satisfied with. However, item …
assists users to purchase products that they are satisfied with. However, item …
Recommendation model based on multi-grained interaction that fuses users' dynamic interests
Z Yang, Y Wang, G Liu, Z Li, X Wang - International Journal of Machine …, 2023 - Springer
Users leave many reviews while participating in network activities, and these have been
proven to improve the performance of recommendation systems. However, most current …
proven to improve the performance of recommendation systems. However, most current …
MRMRP: multi-source review-based model for rating prediction
Reviews written by users often contain rich semantic information which can reflect users'
preferences for different attributes of items. For the past few years, many studies in …
preferences for different attributes of items. For the past few years, many studies in …
A collaborative filtering recommendation system for rating prediction
Recommendation system is a subclass of information filtering system to help users find
relevant items of interest from a large set of possible selections. Model-based collaborative …
relevant items of interest from a large set of possible selections. Model-based collaborative …
Movie Rating Prediction Recommendation Algorithm based on XGBoost-DNN
S Yu, J Qiu, X Bao, M Guo, X Chen… - 2022 12th International …, 2022 - ieeexplore.ieee.org
In the traditional movie recommendation, because the features of users and movies are not
considered, only the users' ratings of movies are considered, so there is a problem that the …
considered, only the users' ratings of movies are considered, so there is a problem that the …
A novel deep recommend model based on rating matrix and item attributes
Traditional recommendation systems only consider the content of users to predict the rating
of items in the recommendation process, and ignore the impact of other factors on the …
of items in the recommendation process, and ignore the impact of other factors on the …
A two-stage rating prediction approach based on matrix clustering on implicit information
W Zhang, X Li, J Li, Y Yang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Traditional matrix factorization (MF) methods take a global view on the user-item rating
matrix to conduct matrix decomposition for rating approximation. However, there is an …
matrix to conduct matrix decomposition for rating approximation. However, there is an …