Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space-Reference-Cited by-同舟云学术

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Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space

Published:2022 Issue: Volume:10 Page:12503-12509
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hirakawa Taisei¹^ORCID,Maeda Keisuke²^ORCID,Ogawa Takahiro³^ORCID,Asamizu Satoshi⁴,Haseyama Miki³^ORCID

Affiliation:

1. Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan

2. Office of Institutional Research, Hokkaido University, Sapporo, Japan

3. Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan

4. Kushiro College, National Institute of Technology, Kushiro, Japan

Funder

Japan Society for the Promotion of Science (JSPS) KAKENHI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09676663.pdf?arnumber=9676663

Reference29 articles.

1. Methods and metrics for cold-start recommendations

2. Matrix Factorization Techniques for Recommender Systems

3. Facing the cold start problem in recommender systems

4. Cross Domain Sentiment Classification Techniques: A Review

5. Cross-Domain Recommendation via Preference Propagation GraphNet

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1. Efficient Apriori Rank Pruning Model Based Novelty Detection with One-Class SVM IN Online Pharmaceutical Platforms;Lecture Notes in Networks and Systems;2024

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