201 Mission Street
San Francisco, CA
Question Identification on Twitter
Irwin King, Ed Chang, Baichuan Li, Xiance Si, Michael Lyu
CIKM2011,
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in CIKM2011 , 2011-10-24.
{}
Probabilistic Factor Models for Web Site Recommendation
Hao Ma, Chao Liu, Irwin King, Michael R. Lyu
ACM SIGIR 2011,
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM SIGIR 2011 , 2011-07-24.
{Due to the prevalence of personalization and information filtering applications, modeling users� interests on the Web has become increasingly important during the past few years. In this paper, aiming at providing accurate personalized Web site recommendations for Web users, we propose a novel probabilistic factor model based on dimensionality reduction techniques. We also extend the proposed method to collective probabilistic factor modeling, which further improves model performance by incorporating heterogeneous data sources. The proposed method is general, and can be applied to not only Web site recommendations, but also a wide range of Web applications, including behavioral targeting, sponsored search, etc. The experimental analysis on Web site recommendation shows that our method outperforms other traditional recommendation approaches. More- over, the complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations.}
Learning to Suggest Questions in Online Forums
Tom Chao Zhou, Chin-Yew Lin, Irwin King, Michael R. Lyu, Young-In Song, Yunbo Cao
AAAI-2011,
2011.
[PDF]
[BIB]
AAAI Copyright
The definitive version was published in AAAI-2011. , 2011-08-07, http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3672
{Online forums contain interactive and semantically related discussions on various questions. Extracted question-answer archive is invaluable knowledge, which can be used to im- prove Question Answering services. In this paper, we address the problem of Question Suggestion, which targets at sug- gesting questions that are semantically related to a queried question. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm be- tween semantically related questions. Therefore, we present a new framework to suggest questions, and propose the Topic- enhanced Translation-based Language Model (TopicTRLM) which fuses both the lexical and latent semantic knowledge. Extensive experiments have been conducted with a large real world data set. Experimental results indicate our approach is very effective and outperforms other popular methods in sev- eral metrics.}
Improving Question Retrieval in Community Question Answering with Label Ranking
Wei Wang, Baichuan Li, Irwin King
International Joint Conference on Neural Network (IJCNN) 2011,
2011.
[PDF]
[BIB]
IEEE Copyright
This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in International Joint Conference on Neural Network (IJCNN) 2011. , 2011-07-30
{}
Enrichment and Reductionism: Two Complementary Approaches for Web Query Classification
Ritesh Agrawal, Irwin King, Remi Zajac, Xiaofeng Yu
ICONIP2011,
2011.
[PDF]
[BIB]
Springer-Verlag Copyright
The definitive version was published in ICONIP2011. , 2011-11-17
{Classifying web queries to predefined target categories,also known as web query classification [10], is important to improve search relevance and also for targeted online advertising. However, web queries are typically short, ambiguous and in constant flux. Moreover, often the target categories lack semantic descriptions. These challenges make the web query classification a non-trivial challenge. In this paper, we present two complementary approaches - enrichment and reductionist - for web query classification. The enrichment method uses World Wide Web (WWW) to build word clouds of target categories, which are then compared to web queries and further uses bayesian framework to classify a given query. The reductionist approach, also referred as the centroid method, works by reducing web queries to few central tokens that rep- resents the query. The two approaches are complementary to each other as the enrichment method exhibit high recall but low precision, whereas the centroid method has high precision but low recall. We evaluate the two approaches based on 1300 human labelled web queries.}
