Improved Metric-Based Recommender by Historical Interactions
A remarkable success in recommendations has been achieved by using methods based on metric learning, especially in digital marketing. However, the existing methods do not consider the relative preferences among items that users like. To overcome this issue, we propose an improved recommender model....
Main Authors: | Yubo Jiang, Yunfang Zhu, Xin Du, Tao Jin |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8822676/ |
Similar Items
-
EPIDEMIOLGICAL STUDIES ON BACTERIAL SKIN INFECTIONS IN DOGS
by: V. H. Shyma, et al.
Published: (2012-01-01) -
Validation on Residual Variation and Covariance Matrix of USSTRATCOM Two Line Element
by: Hyeonjeong Yim, et al.
Published: (2012-09-01) -
Developing a Comprehensive Method for the Fuzzy Analytic Hierarchy Process
(With emphasis on revising of fuzzy inconsistency pair wise comparison matrix)
by: Ali Reza Bafandeh, et al.
Published: (2007-11-01) -
A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem
by: Weiming Huang, et al.
Published: (2024-01-01) -
Keyphrase Ranking Based on Second Order Co-Occurrence Analysis
by: Hosein Shahsavar Haghighi, et al.
Published: (2015-12-01)