Combining long-term and short-term user interest for personalized hashtag recommendation

doi:10.1007/s11704-015-4284-x

Front. Comput. Sci.

2015, Vol. 9

Issue (4) : 608-622 https://doi.org/10.1007/s11704-015-4284-x

RESEARCH ARTICLE

Combining long-term and short-term user interest for personalized hashtag recommendation

Jianjun YU¹,Tongyu ZHU^2,^*(

)

1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
2. State Key Lab of Software Development Environment, Beihang University, Beijing 100190, China

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Abstract

Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages (tweets) on micro-blogging systems as they present rich sentiment information on topics that people are interested in. In this paper, we focus on the problem of hashtag recommendation considering their personalized and temporal aspects. As far as we know, this is the first work addressing this issue specially to recommend personalized hashtags combining longterm and short-term user interest.We introduce three features to capture personal and temporal user interest: 1) hashtag textual information; 2) user behavior; and 3) time. We offer two recommendation models for comparison: a linearcombined model, and an enhanced session-based temporal graph (STG) model, Topic-STG, considering the features to learn user preferences and subsequently recommend personalized hashtags. Experiments on two real tweet datasets illustrate the effectiveness of the proposed models and algorithms.

Keywords recommendation hashtag time-sensitive user interest

Corresponding Author(s): Tongyu ZHU

Just Accepted Date: 31 December 2014 Issue Date: 07 September 2015

Cite this article:

Jianjun YU,Tongyu ZHU. Combining long-term and short-term user interest for personalized hashtag recommendation[J]. Front. Comput. Sci., 2015, 9(4): 608-622.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-015-4284-x
https://academic.hep.com.cn/fcs/EN/Y2015/V9/I4/608

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