Bringing order to the Web: automatically categorizing search results

By Hao Chen, Susan T. Dumais. 2000

479 citations recorded by the source corpus.

We developed a user interface that organizes Web search results into hierarchical categories. Text classification algorithms were used to automatically classify arbitrary search results into an existing category structure on-the-fly. A user study compared our new category interface with the typical ranked list interface of search results. The study showed that the category interface is superior both in objective and subjective measures. Subjects liked the category interface much better than the list interface, and they were 50% faster at finding information that was organized into categories. Organizing search results allows users to focus on items in categories of interest rather than having to browse through all the results sequentially.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN (2018, background): “When there are many labels, hierarchical categorization of texts has been recognized as a natural and effective way to organize texts and it has been well studied in the past two decades [5, 6, 13, 30, 39, 43, 44].”
Query enrichment for web-query classification (2006, methodology): “Instead of classifying queries directly, some previous work focuses on classifying search results as an alternative way of understanding queries [ Chen and Dumais 2000 ].”
A Study of Approaches to Hypertext Categorization (2002, unknown): “A recent user study (Chen and Dumais, 2000) showed that users often prefer navigating through directories of pre-classified content, and that providing a category-based view of retrieved documents enables them to find more relevant information in a shorter time.”
Towards Explainable Text Classification by Jointly Learning Lexicon and Modifier Terms (2017, methodology): “, 2014] have helped improve document navigation tasks by categorizing web search results [Chen and Dumais, 2000], analyzed corpora to identify anonymous authors [Diederich et al.”
Hierarchical classification of Web content (2000, methodology): “Dumais, S. T., Platt, J., Heckerman, D. and Sahami, M. Inductive learning algorithms and representations for text categorization.”
A personalized search engine based on Web‐snippet hierarchical clustering (2005, background): “Any “ﬁxed set of labels” as used in [3, 7] would be not ﬂexible enough to capture the snippets’ themes; , due to computational reasons, the clustering engine must process only the (short and thus poor) snippets and not their corresponding Vivisimo Mooter Copernic iBoogie Kartoo Groxis Dog-pile and…”
Q2C@UST: our winning solution to query classification in KDDCUP 2005 (2005, background): “Ever since the early papers by the pioneers [14][18][22], people have recognized the possibility of conducting Web search through classification, and vice versa [2][5][6][15].”
A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections (2002, methodology): “The automated hierarchic organisation of a collection of documents is an important and timely subject of study in IR (Dumais and Chen, 2000) for example the automatic hierarchic organisation of web search results (Chen and Dumais, 2000).”
Find-similar: similarity browsing as a search tool (2006, background): “These approaches are diﬀerent from ﬁnd-similar in that while the user gets to see documents grouped by similarity, the user does not get to request more documents similar to a document.”
Personalized recommendation in social tagging systems using hierarchical clustering (2008, background): “In [3] clusters of resources are shown to improve recommendation by categorizing the resources into topic domains.”
Support vector machines classification with a very large-scale taxonomy (2005, methodology): “Hierarchical SVMs refer to those methods that decompose the training tasks according to the structure of the taxonomy [4][5][6][10][17][19].”