@inproceedings{qiuyu-etal-2024-knowledge,
title = "Knowledge Graph-Enhanced Recommendation with Box Embeddings",
author = "Qiuyu, Liang and
Weihua, Wang and
Lei, Lv and
Feilong, Bao",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.91/",
pages = "1172--1182",
language = "eng",
abstract = "{\textquotedblleft}Knowledge graphs are used to alleviate the problems of data sparsity and cold starts in recom-mendation systems. However, most existing approaches ignore the hierarchical structure of theknowledge graph. In this paper, we propose a box embedding method for knowledge graph-enhanced recommendation system. Specifically, the box embedding represents not only the in-teraction between the user and the item, but also the head entity, the tail entity and the relationbetween them in the knowledge graph. Then the interaction between the item and the corre-sponding entity is calculated by the multi-task attention unit. Experimental results show thatour method provides a large improvement over previous models in terms of Area Under Curve(AUC) and accuracy in publicly available recommendation datasets with three different domains.{\textquotedblright}"
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="qiuyu-etal-2024-knowledge">
<titleInfo>
<title>Knowledge Graph-Enhanced Recommendation with Box Embeddings</title>
</titleInfo>
<name type="personal">
<namePart type="given">Liang</namePart>
<namePart type="family">Qiuyu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wang</namePart>
<namePart type="family">Weihua</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lv</namePart>
<namePart type="family">Lei</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bao</namePart>
<namePart type="family">Feilong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">eng</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maosong</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiye</namePart>
<namePart type="family">Liang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xianpei</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhiyuan</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yulan</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Taiyuan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“Knowledge graphs are used to alleviate the problems of data sparsity and cold starts in recom-mendation systems. However, most existing approaches ignore the hierarchical structure of theknowledge graph. In this paper, we propose a box embedding method for knowledge graph-enhanced recommendation system. Specifically, the box embedding represents not only the in-teraction between the user and the item, but also the head entity, the tail entity and the relationbetween them in the knowledge graph. Then the interaction between the item and the corre-sponding entity is calculated by the multi-task attention unit. Experimental results show thatour method provides a large improvement over previous models in terms of Area Under Curve(AUC) and accuracy in publicly available recommendation datasets with three different domains.”</abstract>
<identifier type="citekey">qiuyu-etal-2024-knowledge</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-1.91/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>1172</start>
<end>1182</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Knowledge Graph-Enhanced Recommendation with Box Embeddings
%A Qiuyu, Liang
%A Weihua, Wang
%A Lei, Lv
%A Feilong, Bao
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F qiuyu-etal-2024-knowledge
%X “Knowledge graphs are used to alleviate the problems of data sparsity and cold starts in recom-mendation systems. However, most existing approaches ignore the hierarchical structure of theknowledge graph. In this paper, we propose a box embedding method for knowledge graph-enhanced recommendation system. Specifically, the box embedding represents not only the in-teraction between the user and the item, but also the head entity, the tail entity and the relationbetween them in the knowledge graph. Then the interaction between the item and the corre-sponding entity is calculated by the multi-task attention unit. Experimental results show thatour method provides a large improvement over previous models in terms of Area Under Curve(AUC) and accuracy in publicly available recommendation datasets with three different domains.”
%U https://aclanthology.org/2024.ccl-1.91/
%P 1172-1182
Markdown (Informal)
[Knowledge Graph-Enhanced Recommendation with Box Embeddings](https://aclanthology.org/2024.ccl-1.91/) (Qiuyu et al., CCL 2024)
ACL
- Liang Qiuyu, Wang Weihua, Lv Lei, and Bao Feilong. 2024. Knowledge Graph-Enhanced Recommendation with Box Embeddings. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 1172–1182, Taiyuan, China. Chinese Information Processing Society of China.