|
|
Publication
|
| Title of Article |
Dynamic Collaborative Filtering Based on User
Preference Drift and Topic Evolution |
| Date of Acceptance |
3 May 2020 |
| Journal |
| Title of Journal |
IEEE ACCESS |
| Standard |
ISI |
| Institute of Journal |
IEEE Xplore |
| ISBN/ISSN |
2169-3536 |
| Volume |
2020 |
| Issue |
vol. 8 |
| Month |
05 |
| Year of Publication |
2020 |
| Page |
86433 - 86447 |
| Abstract |
Recommender systems are efficient tools for online applications; these systems exploit
historical user ratings on items to make recommendations of items to users. This paper aims to enhance
dynamic collaborative filtering on recommender systems under volatile conditions in which both users’
preferences and item properties dynamically change over time. Moreover, existing collaborative filtering
models mainly rely on solving data sparsity by adding side information to improve performance. We propose
a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to
extract user latent transition patterns and combine latent factors together with the associated topic evolution
of review texts by using topic modeling based on the dynamic environment. We evaluate the accuracy on
real datasets, and the experimental results show that the model leads to a significant improvement compared
with the state-of-the-art dynamic CF models. |
| Keyword |
Recommender systems, user preference drift, topic evolution, dynamic collaborative filtering, data sparsity |
| Author |
|
| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
|
| Citation |
0
|
|
|
|
|
|
|
|
|
Journal Publication
ไทย