A picture of search

By Greg Pass, Abdur Chowdhury, Cayley Torgeson. 2006

747 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Information Retrieval & Search

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 Management Science and Operations Research

In Information Retrieval & Search

Citations in the corpus

Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “hows the effectiveness of using document titles for weak supervision. Rows (2b) and (2c) show the effectiveness of using pseudo-relevant documents, with different queries. In (2b), the AOL query log [Pass et al., 2006] is used, while in (2c), queries from the training set of the MS MARCO document re”
Neural Ranking Models with Weak Supervision (2017, methodology): “0%. „e statistics of these collections are reported in Table 1. Trainingqueryset.To train our neural ranking models, we used the unique queries (only the query string) appearing in the AOL query logs [30]. „is query set contains web queries initiated by real users in the AOL search engine that were ”
Fidelity-Weighted Learning (2017, methodology): “Data with weak labels We create a query set Q using the unique queries appearing in the AOL query logs (Pass et al., 2006).”
Can all tags be used for search? (2008, methodology): “We used the AOL query logs [18] to calculate overlap between Web queries and tags, and contrasted tag and query classes.”
Discovering and understanding word level user intent in Web search queries (2015, methodology): “[34] and analysis on own log), duplicate queries were retained for all experiments.”
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing (2018, methodology): “Training Queries Similar to prior work on weak supervision for information retrieval [16, 52, 53], we generate our weakly labeled data using several million unique queries obtained from the publicly available AOL query logs [38].”
Decoding billions of integers per second through vectorization (2012, methodology): “Nevertheless, to model more closely the performance of a major search engine, we used the AOL query log data set as a collection of query statistics [52, 53].”
Detecting and Defending Against Third-Party Tracking on the Web (2012, methodology): “In order to better approximate a real user’s browsing history, we collected data using the 2006 AOL search query logs [20].”
Analyzing and evaluating query reformulation strategies in web search logs (2009, methodology): “Of the roughly 2 billion daily web searches made by internet users [8], approximately 28% are modifications to the previous query [29], also known as query reformulations or query refinements .”
MIMICS: A Large-Scale Data Collection for Search Clarification (2020, methodology): “t particularly focus on clarification, there might be some connections between them and MIMICS that can be used in future research. In addition, the public query logs, such as the one released by AOL [19], can be used together with MIMICS for further investigations. This also holds for the datasets ”
Can social bookmarking improve web search? (2008, methodology): “We also use the AOL query dataset [13] for certain analyses (Results 1, 3, and 6).”
Robust defenses for cross-site request forgery (2008, methodology): “All state-modifying requests, including login requests, must be sent using the POST method [5].”
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing (2018, neutral): “Training Queries Similar to prior work on weak supervision for information retrieval [16, 52, 53], we generate our weakly labeled data using several million unique queries obtained from the publicly available AOL query logs [38].”
Why we search: visualizing and predicting user behavior (2007, methodology): “The second dataset was extracted from the AOL query logs [ 19 ].”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, background): “5 A cloud of the 250 most frequent queried terms in the AOL query log [160].”
Content-Based Weak Supervision for Ad-Hoc Re-Ranking (2017, methodology): “This source uses the AOL query log [12] as the basis for a ranking-based source, following the approach of [5].”