User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering
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,...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-01-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/12/1/121 |
| _version_ | 1818035140165632000 |
|---|---|
| author | Wenchuan Shi Liejun Wang Jiwei Qin |
| author_facet | Wenchuan Shi Liejun Wang Jiwei Qin |
| author_sort | Wenchuan Shi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T06:50:19Z |
| format | Article |
| id | doaj.art-b01316260df747bc80adc3fa2e669334 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-12-10T06:50:19Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-b01316260df747bc80adc3fa2e6693342022-12-22T01:58:34ZengMDPI AGSymmetry2073-89942020-01-0112112110.3390/sym12010121sym12010121User Embedding for Rating Prediction in SVD++-Based Collaborative FilteringWenchuan Shi0Liejun Wang1Jiwei Qin2School of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaSchool of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaSchool of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaThe 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.https://www.mdpi.com/2073-8994/12/1/121recommendation systemrating predictionsvd++user embedding |
| spellingShingle | Wenchuan Shi Liejun Wang Jiwei Qin User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering Symmetry recommendation system rating prediction svd++ user embedding |
| title | User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering |
| title_full | User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering |
| title_fullStr | User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering |
| title_full_unstemmed | User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering |
| title_short | User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering |
| title_sort | user embedding for rating prediction in svd based collaborative filtering |
| topic | recommendation system rating prediction svd++ user embedding |
| url | https://www.mdpi.com/2073-8994/12/1/121 |
| work_keys_str_mv | AT wenchuanshi userembeddingforratingpredictioninsvdbasedcollaborativefiltering AT liejunwang userembeddingforratingpredictioninsvdbasedcollaborativefiltering AT jiweiqin userembeddingforratingpredictioninsvdbasedcollaborativefiltering |