Learning to cluster web search results

By Hua-Jun Zeng, Qi-Cai He, Zheng Chen, Wei-Ying Ma, Jinwen Ma. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004

688 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/86697d3a3b461f17a399308ed444b79367345380

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. Xiangnan He
2. Tat-seng Chua
3. Lei Zhang
4. Choon Hui Teo
5. Min-Yen Kan
6. Yi Chang
7. Wei-Ying Ma
8. Alex Smola

In Information Systems

1. Xiangnan He
2. Min-Yen Kan
3. Tat-seng Chua
4. Craig Macdonald
5. Iadh Ounis
6. Yi Chang
7. Choon Hui Teo
8. Alex Smola

In Signal Processing

1. Craig Macdonald
2. Ricardo A. Baeza-Yates
3. Yi Chang
4. Iadh Ounis
5. Wei-Ying Ma
6. Zhaohui Zheng
7. Xiao Chen
8. Wee Keong Ng

Citations in the corpus

• Automatic identification of user goals in Web search (2005, background): “…Los Angeles, CA 90095 Los Angeles, CA 90095 Los Angeles, CA 90095 uclee@cs.ucla.edu vicliu@cs.ucla.edu cho@cs.ucla.edu ABSTRACT There have been recent interests in studying the goal be­hind a user s Web query, so that this goal can be used to im­prove the quality of a search engine s results.”
• A personalized search engine based on Web‐snippet hierarchical clustering (2005, neutral): “It may be considered”
• Scalable clustering of news search results (2011, background): “Most of the work on search result clustering focused more on salient feature extraction and utilizing this information to cluster the search results [22].”
• Learning to classify short and sparse text & web with hidden topics from large-scale data collections (2008, background): “Many clustering techniques were proposed to place search results into topic– oriented clusters [25, 35, 36].”
• A survey on data stream clustering and classification (2015, background): “[103] clustered web search results to facilitate users’ quick browsing through search results.”
• AnnoSearch: Image Auto-Annotation by Search (2006, background): “( ) * arg max ( | ) arg max ( | ) ( | ) (*) arg max ( | ) ( | ) ( | ) (**) q i i qi j j i i qi j p I p I p I I p t p t I p I I = = ⋅ = ⋅ ⋅ ∑ ∑ ∑ w w w w w w w (1) (*) corresponds to a two-layer model which directly learns the projections from images to keywords.”
• Getting inspired!: understanding how and why examples are used in creative design practice (2009, background): “[22]) may not always be optimal for example search.”
• Extracting semantic relations from query logs (2007, methodology): “clicked pages). Another related paper denes a query taxonomy to cluster the answers [ 18 ], but query logs are not used, while in Cid et al. [5] they use query logs to maintain a taxonomy, but not to build one.”
• Exploiting query reformulations for web search result diversification (2010, background): “niques [26] in order to generate ‘expanded sub-queries’ from the top retrieved documents in a baseline ranking, or from different document clusters identified in this ranking [27, 32].”
• Comment-based multi-view clustering of web 2.0 items (2014, background): “It has improved retrieval effectiveness for text [41], images [22] and videos [17].”