CLC number: TP391
On-line Access: 2020-03-18
Received: 2018-06-08
Revision Accepted: 2018-12-08
Crosschecked: 2019-08-14
Cited: 0
Clicked: 5582
Wei-ming Lu, Jia-hui Liu, Wei Xu, Peng Wang, Bao-gang Wei. EncyCatalogRec: catalog recommendation for encyclopedia article completion[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(3): 436-447.
@article{title="EncyCatalogRec: catalog recommendation for encyclopedia article completion",
author="Wei-ming Lu, Jia-hui Liu, Wei Xu, Peng Wang, Bao-gang Wei",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="3",
pages="436-447",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800363"
}
%0 Journal Article
%T EncyCatalogRec: catalog recommendation for encyclopedia article completion
%A Wei-ming Lu
%A Jia-hui Liu
%A Wei Xu
%A Peng Wang
%A Bao-gang Wei
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 3
%P 436-447
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800363
TY - JOUR
T1 - EncyCatalogRec: catalog recommendation for encyclopedia article completion
A1 - Wei-ming Lu
A1 - Jia-hui Liu
A1 - Wei Xu
A1 - Peng Wang
A1 - Bao-gang Wei
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 3
SP - 436
EP - 447
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800363
Abstract: Online encyclopedias such as Wikipedia provide a large and growing number of articles on many topics. However, the content of many articles is still far from complete. In this paper, we propose EncyCatalogRec, a system to help generate a more comprehensive article by recommending catalogs. First, we represent articles and catalog items as embedding vectors, and obtain similar articles via the locality sensitive hashing technology, where the items of these articles are considered as the candidate items. Then a relation graph is built from the articles and the candidate items. This is further transformed into a product graph. So, the recommendation problem is changed to a transductive learning problem in the product graph. Finally, the recommended items are sorted by the learning-to-rank technology. Experimental results demonstrate that our approach achieves state-of-the-art performance on catalog recommendation in both warm- and cold-start scenarios. We have validated our approach by a case study.
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