Query Recommendation Using Query Logs in Search Engines

By Ricardo A. Baeza-Yates, Carlos A. Hurtado, Marcelo Mendoza. EDBT Workshops, 2004

731 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/60b249993cc7245969c6ed16bd0c233593c88cfe

How researchers see this paper

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

In Information Systems

In Signal Processing

Citations in the corpus

The query-flow graph: model and applications (2008, background): “Most of the work on query recommendation is focused on measures of query similarity [23, 11] that can be used for query expansion [3] or query clustering [3, 22].”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, methodology): “Roughly speaking, drawing suggestions from queries submitted in the past can be interpreted as a way of “exploiting queries submitted by experts to help non-expert users [17].”
A survey of community search over big graphs (2019, background): “To suggest query parameters, a possible research direction is to exploit historical query logs and suggest some values of parameters automatically [13,140].”
Exploiting query reformulations for web search result diversification (2010, background): “to the initial query in a bipartite click-through graph [3, 31].”
A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion (2015, methodology): “Query suggestion algorithms use clustering methods to ﬁnd similar queries so that they can be used as suggestions for one another [1, 40].”
Query Expansion Techniques for Information Retrieval: a Survey (2017, neutral): “Some researchers refer to query logs as user logs since it is derived from historical records of user queries registered in the query logs of search engine (e.g., [74,38,18,282]).”
An Overview of Microsoft Academic Service (MAS) and Applications (2015, neutral): “miss” fashion [4, 7].”
Recommender Systems (2016, neutral): “The work in [57] returns the most similar queries to the”
Data services leveraging Bing's data assets (2016, background): “The problem of finding semantically similar/related queries is related to synonym-finding, and is extensively studied in the literature [3, 4, 12].”
Analyzing and evaluating query reformulation strategies in web search logs (2009, methodology): “For example, work by Baeza-Yates et al. [6] uses query logs to discover new query reformulations, finding similar queries using a cosine function over a term-weighted vector built from the clicked documents.”
Query suggestion using hitting time (2008, background): “While there are quite a few work on generating different types of query suggestions, such as query auto completion [7, 8], query spelling correction [10, 15], query expansion [21, 22, 3], and query rewriting [1, 13].”
Real-time automatic tag recommendation (2008, background): “Here, we address the problem of automatic tag recommendation for document search engines and digital libraries, which bears some similarities to that of query recommendation [1].”
Sequence-Aware Recommender Systems (2018, background): “Examples include advertisement recommendation [121], news recommendation [32], job posting recommendation [91], the recommendation of videos in streaming platforms [46, 91], query recommendation [8, 18, 42, 102], or the recommendation of next activities to add during workflow modeling [59].”