Orthogonal query recommendation

Vahabi, Hossein; Ackerman, Margareta; Loker, David; Baeza-Yates, Ricardo; López-Ortíz, Alejandro

doi:10.1145/2507157.2507159

Cited by 22 publications

(14 citation statements)

References 12 publications

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“…ese methods perform poorly for long-tail queries. To tackle this issue, Vahabi et al [45] proposed to nd suggestions for long-tail queries by comparing their search results. Another group of research tries to generate new suggestions by leveraging search logs and external resources.…”

Section: Related Workmentioning

confidence: 99%

Learning to Attend, Copy, and Generate for Session-Based Query Suggestion

Dehghani

Rothe

Alfonseca

et al. 2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more e ective, search engines provide related queries to help users in specifying the information need in their search process. In this paper we propose a customized sequence-to-sequence model for sessionbased query suggestion. In our model, we employ a query-aware a ention mechanism to capture the structure of the session context. is enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore we observe that, based on the user query reformulation behavior, within a single session a large portion of query terms is retained from the previously submi ed queries and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion.

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Section: Related Workmentioning

confidence: 99%

Learning to Attend, Copy, and Generate for Session-Based Query Suggestion

Dehghani

Rothe

Alfonseca

et al. 2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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“…In the case of query suggestion the objective is to provide users with queries that are important for meeting users' information needs [2,7,39], while in query rewriting the task is to expand query to increase both the number and quality of retrieved ads. We refer an interested reader to [36] for an overview of query log mining approaches as applied to query suggestion task.…”

Section: Query Rewriting For Sponsored Searchmentioning

confidence: 99%

Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search

Grbovic

Djuric

Radosavljević

et al. 2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Search engines represent one of the most popular web services, visited by more than 85% of internet users on a daily basis. Advertisers are interested in making use of this vast business potential, as very clear intent signal communicated through an issued query allows effective targeting of users. This idea is embodied in a sponsored search model, where each advertiser maintains a list of keywords they deem indicative of increased user response rate with regards to their business. According to this targeting model, when a query is issued all advertisers with a matching keyword are entered into an auction according to the amount they bid for the query and the winner gets to show their ad. One of the main challenges is the fact that a query may not match many keywords, resulting in lower auction value, lower ad quality, and lost revenue for advertisers and publishers. Possible solution is to expand a query into a set of related queries and use them to increase the number of matched ads, called query rewriting. To this end, we propose rewriting method based on a novel query embedding algorithm, which jointly models query content as well as its context within a search session. As a result, semantically similar queries are mapped into vectors close in the embedding space, which allows expansion of a query via simple K-nearest neighbor search. The method was trained on more than 12 billion sessions, one of the largest corpus reported thus far, and evaluated on both public TREC data set and an in-house sponsored search data set. The results show that the proposed approach significantly outperformed existing state-of-the-art, strongly indicating its benefits and monetization potential.

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“…Hossein et al [9] have presented a new query recommendation technique based on identifying orthogonal queries in an ad-hoc query answer cache. This approach requires no training, is computationally efficient, and can be easily integrated into any search engine with an answer cache.…”

Section: Related Workmentioning

confidence: 99%

Query Click and Text Similarity Graph for Query Suggestions

Sejal

Shailesh

Tejaswi

et al. 2015

Machine Learning and Data Mining in Pattern Recognition

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Abstract. Query suggestion is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of suggestions like query, image, movies, music and book etc. are used every day. Various types of data sources are used for the suggestions. If we model the data into various kinds of graphs then we can build a general method for any suggestions. In this paper, we have proposed a general method for query suggestion by combining two graphs: (1) query click graph which captures the relationship between queries frequently clicked on common URLs and (2) query text similarity graph which finds the similarity between two queries using Jaccard similarity. The proposed method provides literally as well as semantically relevant queries for users' need. Simulation results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It can be used for recommendation tasks like query, image, and product suggestion.

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Orthogonal query recommendation

Cited by 22 publications

References 12 publications

Learning to Attend, Copy, and Generate for Session-Based Query Suggestion

Learning to Attend, Copy, and Generate for Session-Based Query Suggestion

Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search

Query Click and Text Similarity Graph for Query Suggestions

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