Sense-Based Topic Word Embedding Model for Item Recommendation
As a useful way to help users filter information and save time, item recommendation intends to recommend new items to users who tend to be interested. As the most common format related to items in online social networks, short texts have always been disregarded by previous research on item recommend...
Main Authors: | Ya Xiao, Zhijie Fan, Chengxiang Tan, Qian Xu, Wenye Zhu, Fujia Cheng |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8682139/ |
Similar Items
-
New Vector-Space Embeddings for Recommender Systems
by: Sandra Rizkallah, et al.
Published: (2021-07-01) -
Topic Modeling for Short Texts via Word Embedding and Document Correlation
by: Feng Yi, et al.
Published: (2020-01-01) -
Polysemy Needs Attention: Short-Text Topic Discovery With Global and Multi-Sense Information
by: Heng-Yang Lu, et al.
Published: (2021-01-01) -
Collaboratively Modeling and Embedding of Latent Topics for Short Texts
by: Zheng Liu, et al.
Published: (2020-01-01) -
Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems
by: T. Nguyen, et al.
Published: (2018-12-01)