Learning query intent from regularized click graphs

By Xiao Li, Ye-Yi Wang, A. Acero. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008

389 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/d25b816de84af3493aa2899baa4eb4d539e188c9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tao Mei
2. Ting Yao
3. Yingwei Pan
4. Jun Xu
5. Bing Liu
6. B. Liu
7. Zhiyuan Chen
8. Hang Li

In Computer Vision and Pattern Recognition

1. Tao Mei
2. Yi Yang
3. Luc Van Gool
4. Dacheng Tao
5. Ting Yao
6. Xiaogang Wang
7. Trevor Darrell
8. Yingwei Pan

In Information Systems

1. Jun Xu
2. Hang Li
3. Yong Rui
4. Meichun Hsu
5. Riddhiman Ghosh
6. Malú Castellanos
7. Wayne Xin Zhao
8. Zhenguo Li

Citations in the corpus

• Understanding user's query intent with wikipedia (2009, methodology): “Previous works [1] and [14] focused on solving this problem by augmenting the labeled queries through automatically labeling more user input queries using semi-supervised learning over search log data.”
• Semantic Matching in Search (2014, background): “Query classification [97, 113, 13, 161, 119] and document classification should be helpful for addressing query document mismatch.”
• Seizure prediction with spectral power of EEG using cost‐sensitive support vector machines (2011, methodology): “We use an F b measurement to determine the optimal pair in highly unbalanced datasets: (Van Rijsbergen, 1979; Li et al., 2008), where TP is the number of true positives (the number of preictal windows classified correctly).”
• Identifying Intention Posts in Discussion Forums (2013, background): “Although we have not found any paper studying intention classification of social media posts, there are some related works in the domain of Web search, where user or query intent classification is a major issue (Hu et al., 2009; Li, 2010; Li et al., 2008).”
• Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets (2015, background): “The existing work on query intent classification focuses on expanding features of queries by including external knowledge, such as search snippets from search engine, click-through (Li, Wang, and Acero 2008) and facets from Wikipedia (Hu et al. 2009).”
• A Graph-Based Push Service Platform (2016, background): “The crucial part of push-mode is target user group discovery, which can be solved by rule-based [5], CF-based [12] and graph-based [7,9] approaches.”
• Mining target users for online marketing based on App Store data (2015, neutral): “Several techniques are used to target user groups for an advertisement, including rule-based methods [10], collaborative filtering based recommendation methods [18] and graph based methods [8] [14].”
• Understanding the Semantic Structure of Noun Phrase Queries (2010, background): “As mentioned in the introduction, previous works on query intent understanding have largely focused on classification, i.e. , automatically mapping queries into semantic classes (Shen et al., 2006; Li et al., 2008b; Arguello et al., 2009).”
• Click-through-based cross-view learning for image search (2014, methodology): “presented the use of click graphs in improving query intent classifiers [18].”