Abstract

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there's a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Pages1813-1819
Number of pages7
Volume2015-January
ISBN (Print)9781577357384
StatePublished - 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: Jul 25 2015Jul 31 2015

Other

Other24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CountryArgentina
CityBuenos Aires
Period7/25/157/31/15

Fingerprint

Recommender systems
Availability

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Wang, S., Tang, J., Wang, Y., & Liu, H. (2015). Exploring implicit hierarchical structures for recommender systems. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2015-January, pp. 1813-1819). International Joint Conferences on Artificial Intelligence.

Exploring implicit hierarchical structures for recommender systems. / Wang, Suhang; Tang, Jiliang; Wang, Yilin; Liu, Huan.

IJCAI International Joint Conference on Artificial Intelligence. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. p. 1813-1819.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, S, Tang, J, Wang, Y & Liu, H 2015, Exploring implicit hierarchical structures for recommender systems. in IJCAI International Joint Conference on Artificial Intelligence. vol. 2015-January, International Joint Conferences on Artificial Intelligence, pp. 1813-1819, 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, 7/25/15.
Wang S, Tang J, Wang Y, Liu H. Exploring implicit hierarchical structures for recommender systems. In IJCAI International Joint Conference on Artificial Intelligence. Vol. 2015-January. International Joint Conferences on Artificial Intelligence. 2015. p. 1813-1819
Wang, Suhang ; Tang, Jiliang ; Wang, Yilin ; Liu, Huan. / Exploring implicit hierarchical structures for recommender systems. IJCAI International Joint Conference on Artificial Intelligence. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. pp. 1813-1819
@inproceedings{90069602ec194fee8924bd6b1a0fab68,
title = "Exploring implicit hierarchical structures for recommender systems",
abstract = "Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there's a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework.",
author = "Suhang Wang and Jiliang Tang and Yilin Wang and Huan Liu",
year = "2015",
language = "English (US)",
isbn = "9781577357384",
volume = "2015-January",
pages = "1813--1819",
booktitle = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",

}

TY - GEN

T1 - Exploring implicit hierarchical structures for recommender systems

AU - Wang, Suhang

AU - Tang, Jiliang

AU - Wang, Yilin

AU - Liu, Huan

PY - 2015

Y1 - 2015

N2 - Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there's a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework.

AB - Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there's a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework.

UR - http://www.scopus.com/inward/record.url?scp=84949794248&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949794248&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781577357384

VL - 2015-January

SP - 1813

EP - 1819

BT - IJCAI International Joint Conference on Artificial Intelligence

PB - International Joint Conferences on Artificial Intelligence

ER -