Synthesizing high utility suggestions for rare web search queries

Jain, Ankur; Özertem, Umut; Velipasaoglu, Emre

doi:10.1145/2009916.2010024

Cited by 26 publications

(22 citation statements)

References 32 publications

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“…The method leverages the knowledge extracted from past queries and build a locality sensitive hashing function thrugh which similarity of rare queries is estimated. Jain et al [10] modifies the terms in rare queries in order to match more frequent queries in the query log.…”

Section: Related Workmentioning

confidence: 99%

Efficient query recommendations in the long tail via center-piece subgraphs

Bonchi

Perego

Silvestri

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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We present a recommendation method based on the wellknown concept of center-piece subgraph, that allows for the time/space efficient generation of suggestions also for rare, i.e., long-tail queries. Our method is scalable with respect to both the size of datasets from which the model is computed and the heavy workloads that current web search engines have to deal with. Basically, we relate terms contained into queries with highly correlated queries in a query-flow graph. This enables a novel recommendation generation method able to produce recommendations for approximately 99% of the workload of a real-world search engine. The method is based on a graph having term nodes, query nodes, and two kinds of connections: term-query and query-query. The first connects a term to the queries in which it is contained, the second connects two query nodes if the likelihood that a user submits the second query after having issued the first one is sufficiently high. On such large graph we need to compute the center-piece subgraph induced by terms contained into queries. In order to reduce the cost of the above computation, we introduce a novel and efficient method based on an inverted index representation of the model. We experiment our solution on two real-world query logs and we show that its effectiveness is comparable (and in some case better) than state-of-the-art methods for head-queries. More importantly, the quality of the recommendations generated remains very high also for long-tail queries, where other methods fail even to produce any suggestion. Finally, we extensively investigate scalability and efficiency issues and we show the viability of our method in real world search engines.

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Section: Related Workmentioning

confidence: 99%

Efficient query recommendations in the long tail via center-piece subgraphs

Bonchi

Perego

Silvestri

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Recently, several research works [6,[18][19][20] proposed recommending high utility queries to users. Both studies [6,18] defined a global utility function over the recommended queries set, which emphasize either the diversity [18] or the expected click-through rate [6] of the recommendations.…”

Section: Utility-based Query Recommendationmentioning

confidence: 99%

“…Both studies [6,18] defined a global utility function over the recommended queries set, which emphasize either the diversity [18] or the expected click-through rate [6] of the recommendations. But, they did not define and learn the query utility toward users' postclick experience as ours.…”

Section: Utility-based Query Recommendationmentioning

confidence: 99%

Recommending High Utility Queries via Query-Reformulation Graph

Wang

Huang

2015

Mathematical Problems in Engineering

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Query recommendation is an essential part of modern search engine which aims at helping users find useful information. Existing query recommendation methods all focus on recommending similar queries to the users. However, the main problem of these similarity-based approaches is that even some very similar queries may return few or even no useful search results, while other less similar queries may return more useful search results, especially when the initial query does not reflect user's search intent correctly. Therefore, we propose recommending high utility queries, that is, useful queries with more relevant documents, rather than similar ones. In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. Then, we apply an absorbing random walk on the query-reformulation graph and model the document utility with the transition probability from initial query to the satisfactory document. At last, we propagate the document utilities back to queries and rank candidate queries with their utilities for recommendation. Extensive experiments were conducted on real query logs, and the experimental results have shown that our method significantly outperformed the state-of-the-art methods in recommending high utility queries.

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“…Some algorithms have been developed and published which translate, expand or adjust the query based on large-scale linguistic resources such as search logs and document sets, for example [12,21,16,20]. Many of these rewriting methods can be run quickly, without requiring any extra search against a document corpus, so they are suitable for applications where response time is critical.…”

Section: Introductionmentioning

confidence: 99%

Robust query rewriting using anchor data

Craswell

Billerbeck²,

Fetterly

et al. 2013

Proceedings of the Sixth ACM International Conference on Web Search and Data Mining

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Query rewriting algorithms can be used as a form of query expansion, by combining the user's original query with automatically generated rewrites. Rewriting algorithms bring linguistic datasets to bear without the need for iterative relevance feedback, but most studies of rewriting have used proprietary datasets such as large-scale search logs. By contrast this paper uses readily available data, particularly ClueWeb09 link text with over 1.2 billion anchor phrases, to generate rewrites. To avoid overfitting, our initial analysis is performed using Million Query Track queries, leading us to identify three algorithms which perform well. We then test the algorithms on Web and newswire data. Results show good properties in terms of robustness and early precision.

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Synthesizing high utility suggestions for rare web search queries

Cited by 26 publications

References 32 publications

Efficient query recommendations in the long tail via center-piece subgraphs

Efficient query recommendations in the long tail via center-piece subgraphs

Recommending High Utility Queries via Query-Reformulation Graph

Robust query rewriting using anchor data

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