Recommending Search Queries in Documents Using Inter N-Gram Similarities
Pages 211 - 220
Abstract
Reading a document can often trigger a need for additional information. For example, a reader of a news article might be interested in information about the persons and events mentioned in the article. Accordingly, there is a line of work on recommending search-engine queries given a document read by a user. Often, the recommended queries are selected from a query log independently of each other, and are presented to the user without any context. We address a novel query recommendation task where the recommended queries must be n-grams (sequences of consecutive terms) in the document. Furthermore, inspired by work on using inter-document similarities for document retrieval, we explore the merits of using inter n-gram similarities for query recommendation. Specifically, we use a supervised approach to learn an inter n-gram similarity measure where the goal is that n-grams that are likely to serve as queries will be deemed more similar to each other than to other n-grams. We use the similarity measure in a wide variety of query recommendation approaches which we devise as adaptations of ad hoc document retrieval techniques. Empirical evaluation performed using data gathered from Yahoo!'s search engine logs attests to the effectiveness of the resultant recommendation methods.
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Index Terms
- Recommending Search Queries in Documents Using Inter N-Gram Similarities
Recommendations
Recommending high-utility search engine queries via a query-recommending model
Query recommendation technology is of great importance for search engines, because it can assist users to find the information they require. Many query recommendation algorithms have been proposed, but they all aim to recommend similar queries and ...
A unified framework for recommending diverse and relevant queries
WWW '11: Proceedings of the 20th international conference on World wide webQuery recommendation has been considered as an effective way to help search users in their information seeking activities. Traditional approaches mainly focused on recommending alternative queries with close search intent to the original query. However, ...
Entity-Based Query Recommendation for Long-Tail Queries
Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users’ search history (or query logs), are often insufficient for ...
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Published In
July 2021
334 pages
ISBN:9781450386111
DOI:10.1145/3471158
- General Chair:
- Faegheh Hasibi,
- Program Chairs:
- Yi Fang,
- Akiko Aizawa
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Published: 31 August 2021
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