Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Hu, Guangneng"'
Natural language explanation in visual question answer (VQA-NLE) aims to explain the decision-making process of models by generating natural language sentences to increase users' trust in the black-box systems. Existing post-hoc methods have achieved
Externí odkaz:
http://arxiv.org/abs/2312.13594
Publikováno v:
In Knowledge-Based Systems 9 October 2024 301
Publikováno v:
In Information Fusion October 2024 110
Social recommendation is effective in improving the recommendation performance by leveraging social relations from online social networking platforms. Social relations among users provide friends' information for modeling users' interest in candidate
Externí odkaz:
http://arxiv.org/abs/2103.08976
Autor:
Hu, Guangneng, Yang, Qiang
We investigate how to solve the cross-corpus news recommendation for unseen users in the future. This is a problem where traditional content-based recommendation techniques often fail. Luckily, in real-world recommendation services, some publisher (e
Externí odkaz:
http://arxiv.org/abs/2101.05611
Autor:
Hu, Guangneng, Yang, Qiang
Publikováno v:
Findings of ACL: EMNLP 2020
Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain. Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the sour
Externí odkaz:
http://arxiv.org/abs/2010.08187
Autor:
Hu, Guangneng
Publikováno v:
NAACL 2019
Collaborative filtering (CF) is a core technique for recommender systems. Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and views) only and hence they suffer from the data sparsity issue. Items are usually associated wit
Externí odkaz:
http://arxiv.org/abs/1903.07860
Publikováno v:
WWW 2019
Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information
Externí odkaz:
http://arxiv.org/abs/1901.07199
Publikováno v:
CIKM 2018
The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfer learning is a class of algorithms underlying these techniques. In th
Externí odkaz:
http://arxiv.org/abs/1804.06769
Collaborative filtering (CF) is the key technique for recommender systems. Pure CF approaches exploit the user-item interaction data (e.g., clicks, likes, and views) only and suffer from the sparsity issue. Items are usually associated with content i
Externí odkaz:
http://arxiv.org/abs/1804.06201