Sources of evidence for vertical selection

Abstract

Web search providers often include search services for domainspecific subcollections, called verticals, such as news, images, videos, job postings, company summaries, and artist profiles. We address the problem of vertical selection, predicting relevant verticals (if any) for queries issued to the search engine's main web search page. In contrast to prior query classification and resource selection tasks, vertical selection is associated with unique resources that can inform the classification decision. We focus on three sources of evidence: (1) the query string, from which features are derived independent of external resources, (2) logs of queries previously issued directly to the vertical, and (3) corpora representative of vertical content. We focus on 18 different verticals, which differ in terms of semantics, media type, size, and level of query traffic. We compare our method to prior work in federated search and retrieval effectiveness prediction. An in-depth error analysis reveals unique challenges across different verticals and provides insight into vertical selection for future work.

Task complexity, vertical display and user interaction in aggregated search

“…The task is typically decomposed into two subtasks: predicting which verticals to present (vertical selection) and predicting where in the Web results to present them (vertical presentation). Existing methods for vertical selection and presentation use machine learning to combine different types of predictive evidence: query-string features [2,4,5,19,23], vertical query-log features [2,4,5,11,23], vertical content features [2,4,5,11], and implicit feedback features from previous presentations of the vertical [11,23]. Model tuning and evaluation is typically done with respect to editorial relevance judgements [2,3,4,5,19] or, in a production environment, with respect to user-generated clicks and skips [11,23].…”

“…Most published research in aggregated search has focused on automatic methods for predicting which verticals to present (vertical selection) [4,5,11,19] and where in the Web results to present them (vertical presentation) [2,3,23]. Evaluation of these systems has typically been conducted by using editorial vertical relevance judgements as the gold standard [2,3,4,5,19], or by using user-generated clicks on vertical results as a proxy for relevance [11,23].…”

“…Evaluation of these systems has typically been conducted by using editorial vertical relevance judgements as the gold standard [2,3,4,5,19], or by using user-generated clicks on vertical results as a proxy for relevance [11,23]. While these studies have greatly advanced the state of the art in aggregated search techniques, because users are far removed from the evaluation, they have contributed little insight about how users' higher-level objectives influence their engagement with vertical search results.…”

A Methodology for Evaluating Aggregated Search Results

“…Most prior work focuses on vertical selection-the task of predicting which verticals (if any) are relevant to a query [5,10,1,6,2]. The second task of deciding where in the Web results to embed the vertical results has received less attention.…”

Using Intent Information to Model User Behavior in Diversified Search

“…Since then all major commercial search engines addressed the problem of ambiguous queries either by the technique called federated / vertical search (see, e.g., [2]) or by making result diversification a part of the ranking process [1,9]. In this work we focus on one particular vertical: fresh results, i.e., recently published webpages (news, blogs, etc.).…”