EncyCatalogRec: catalog recommendation for encyclopedia article completion

LU Wei-ming; LIU Jia-hui; XU Wei; WANG Peng; WEI Bao-gang

doi:10.1631/FITEE.1800363

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EncyCatalogRec: catalog recommendation for encyclopedia article completion

Regular Papers | Updated：2022-05-19

- EncyCatalogRec: catalog recommendation for encyclopedia article completion
- EncyCatalogRec：针对百科文章补全的目录推荐
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 3, Pages: 436-447(2020)
- Affiliations：
  
  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  Wei-ming LU, E-mail: luwm@zju.edu.cn
- Funds：
- DOI：10.1631/FITEE.1800363
  CLC： TP391
- Received：08 June 2018，
  
  Revised：2019-;8-14，
  
  Published Online：05 September 2019，
  
  Published：2020-03
- Accepted：
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LU Wei-ming, LIU Jia-hui, XU Wei, et al. EncyCatalogRec: catalog recommendation for encyclopedia article completion[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(3): 436-447.
DOI：

LU Wei-ming, LIU Jia-hui, XU Wei, et al. EncyCatalogRec: catalog recommendation for encyclopedia article completion[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(3): 436-447. DOI： 10.1631/FITEE.1800363.

摘要

目前，在线百科（如维基百科等）已提供海量且主题多样的文章。然而，部分文章内容仍不够完善。本文提出EncyCatalogRec，一种能为百科文章推荐相关目录，从而帮助用户更好完善百科内容的系统。首先，将百科文章和目录项表达为内嵌向量，基于局部敏感哈希方法检索得到相关文章，并以这些文章的目录项为候选项；然后，基于检索得到的文章及其目录项构建关系图，进一步转为乘积图；在乘积图上，将目录推荐问题转为直推式学习问题；最后，基于学习排序算法对推荐得到的目录项排序。热启动和冷启动场景实验均证实，本文所提方法性能优于已有方法。最后通过示例验证了所提方法性能。

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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references

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