Selective Cluster Presentation on the Search Results Page

Levi, Or; Guy, Ido; Raiber, Fiana; Kurland, Oren

doi:10.1145/3158672

Cited by 10 publications

(4 citation statements)

References 70 publications

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“…Originally, these predictors were designed to predict ad hoc document retrieval effectiveness based on information induced from the query and the document corpus without using relevance judgments. They were also used to improve retrieval effectiveness [26,30]. The four prediction values are computed for д ′ using information induced from terms in д ′ with respect to a collection of documents.…”

Section: Learning a Similaritymentioning

confidence: 99%

Recommending Search Queries in Documents Using Inter N-Gram Similarities

Sheetrit¹,

Fyodorov²,

Raiber³

et al. 2021

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval

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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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Section: Learning a Similaritymentioning

confidence: 99%

Recommending Search Queries in Documents Using Inter N-Gram Similarities

Sheetrit¹,

Fyodorov²,

Raiber³

et al. 2021

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval

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“…Most existing approaches use a binary classifier for each vertical to make a decision on whether to present a vertical result in a page [15,19,36]. From this perspective, each classifier can adopt a different feature representation and focus on the features that are uniquely predictive for its corresponding vertical.…”

Section: Vertical Selectionmentioning

confidence: 99%

Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning

Takanobu¹,

Zhuang

Huang

et al. 2019

The World Wide Web Conference

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In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment. First, unlike traditional aggregated web search that merely presents multi-sourced results in the first page, this new task may present aggregated results in all pages and has to dynamically decide which source should be presented in the current page. Second, as pointed out by many existing studies, it is not trivial to rank items from heterogeneous sources because the relevance scores from different source systems are not directly comparable. To address these two issues, we decompose the task into two subtasks in a hierarchical structure: a high-level task for source selection where we model the sequential patterns of user behaviors onto aggregated results in different pages so as to understand user intents and select the relevant sources properly; and a low-level task for item presentation where we formulate a slot filling process to sequentially present the items instead of giving each item a relevance score when deciding the presentation order of heterogeneous items. Since both subtasks can be naturally formulated as sequential decision problems and learn from the future user feedback on search results, we build our model with hierarchical reinforcement learning. Extensive experiments demonstrate that our model obtains remarkable improvements in search performance metrics, and achieves a higher user satisfaction.

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“…Many of the uses of CQA archives suit one of the two types of questions and can therefore benefit from automated classification. Supporting question retrieval (Jeon, Croft, & Lee, 2005) and serving CQA-intent queries on Web search (Levi, Guy, Raiber, & Kurland, 2018), perhaps the two most common practices of CQA archives, correspond to the informational type questions, and with the recent advancements in social media mining, conversational questions in the archives can be used to support a variety of applications, such as opinion mining (Pang & Lee, 2008), controversy detection (Dori-Hacohen & Allan, 2015), and automated debating (Gurevych, Hovy, Slonim, & Stein, 2016).…”

Section: Identifying Informational Vs Conversational Question On Cqa ...mentioning

confidence: 99%

Implicit dimension identification in user-generated text with LSTM networks

Makarenkov

Guy

Hazon

et al. 2019

Information Processing & Management

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In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative.It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers.We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user's knowledge, intent or belief that may be based on writer's moral foundation: 1) political perspective detection in news articles 2) identification of informational vs. conversational questions in community question answering (CQA) archives and. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press.

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Selective Cluster Presentation on the Search Results Page

Cited by 10 publications

References 70 publications

Recommending Search Queries in Documents Using Inter N-Gram Similarities

Recommending Search Queries in Documents Using Inter N-Gram Similarities

Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning

Implicit dimension identification in user-generated text with LSTM networks

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