Micro-Browsing Models for Search Snippets
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Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user. There has been considerable work on user browsing models, to model and analyze both the examination and the relevance components of CTR. In this paper, we propose a novel formulation: a micro-browsing model for how users read result snippets. The snippet text of a result often plays a critical role in the perceived relevance of the result. We study how particular words within a line of snippet can influence user behavior. We validate this new micro-browsing user model by considering the problem of predicting which snippet will yield higher CTR, and show that classification accuracy is dramatically higher with our micro-browsing user model. The key insight in this paper is that varying relatively few words within a snippet, and even their location within a snippet, can have a significant influence on the clickthrough of a snippet.Keywords:
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Web search engines present lists of captions, comprising title, snippet, and URL, to help users decide which search results to visit. Understanding the influence of features of these captions on Web search behavior may help validate algorithms and guidelines for their improved generation. In this paper we develop a methodology to use clickthrough logs from a commercial search engine to study user behavior when interacting with search result captions. The findings of our study suggest that relatively simple caption features such as the presence of all terms query terms, the readability of the snippet, and the length of the URL shown in the caption, can significantly influence users' Web search behavior.
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This paper investigates the effect of a snippet on users' relevance judgment of a document. Web search engines are becoming very useful tools in our daily life. One of the most important features of modern search engines is a snippet, which is expected to help users to find relevant pages immediately. Therefore, improving the quality of a snippet is important for minimizing the cost of user feedback required for finding relevant pages. This paper investigates the effect of a snippet on users' relevance judgment of a document by comparing users' relevance judgment when a snippet is provided and those without providing a snippet. The experimental results show providing snippets reduces users' judgment time, while keeping judgment accuracy. It is also shown the effect of snippet length on judgment time is strong when judging relevant documents. The obtained results will contribute to the improvement of a snippet generation method in terms of minimal user feedback.
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Currently, the quality of a search engine is often determined using so-called topical relevance, i.e., the match between the user intent (expressed as a query) and the content of the document. In this work we want to draw attention to two aspects of retrieval system performance affected by the presentation of results: result attractiveness ("perceived relevance") and immediate usefulness of the snippets ("snippet relevance"). Perceived relevance may influence discoverability of good topical documents and seemingly better rankings may in fact be less useful to the user if good-looking snippets lead to irrelevant documents or vice-versa. And result items on a search engine result page (SERP) with high snippet relevance may add towards the total utility gained by the user even without the need to click those items. We start by motivating the need to collect different aspects of relevance (topical, perceived and snippet relevances) and how these aspects can improve evaluation measures. We then discuss possible ways to collect these relevance aspects using crowdsourcing and the challenges arising from that.
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This paper gives an overview of the INEX 2013 Snippet Retrieval Track. The goal of the Snippet Retrieval Track is to provide a common forum for the evaluation of the e ectiveness of snippets, and to investigate how best to generate snippets for search results. Such snippets should provide the user with su cient information to determine whether the underlying document is relevant. We discuss the setup of the track, details of the assessment and evaluation, and initial results.
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Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user. There has been considerable work on user browsing models, to model and analyze both the examination and the relevance components of CTR. In this paper, we propose a novel formulation: a micro-browsing model for how users read result snippets. The snippet text of a result often plays a critical role in the perceived relevance of the result. We study how particular words within a line of snippet can influence user behavior. We validate this new micro-browsing user model by considering the problem of predicting which snippet will yield higher CTR, and show that classification accuracy is dramatically higher with our micro-browsing user model. The key insight in this paper is that varying relatively few words within a snippet, and even their location within a snippet, can have a significant influence on the clickthrough of a snippet.
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Click-through data has been used in various ways in Web search such as estimating relevance between documents and queries. Since only search snippets are perceived by users before issuing any clicks, the relevance induced by clicks are usually called \emph{perceived relevance} which has proven to be quite useful for Web search. While there is plenty of click data for popular queries, very little information is available for unpopular tail ones. These tail queries take a large portion of the search volume but search accuracy for these queries is usually unsatisfactory due to data sparseness such as limited click information. In this paper, we study the problem of modeling perceived relevance for queries without click-through data. Instead of relying on users' click data, we carefully design a set of snippet features and use them to approximately capture the perceived relevance. We study the effectiveness of this set of snippet features in two settings: (1) predicting perceived relevance and (2) enhancing search engine ranking. Experimental results show that our proposed model is effective to predict the relative perceived relevance of Web search results. Furthermore, our proposed snippet features are effective to improve search accuracy for longer tail queries without click-through data.
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Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user. There has been considerable work on user browsing models, to model and analyze both the examination and the relevance components of CTR. In this paper, we propose a novel formulation: a micro-browsing model for how users read result snippets. The snippet text of a result often plays a critical role in the perceived relevance of the result. We study how particular words within a line of snippet can influence user behavior. We validate this new micro-browsing user model by considering the problem of predicting which snippet will yield higher CTR, and show that classification accuracy is dramatically higher with our micro-browsing user model. The key insight in this paper is that varying relatively few words within a snippet, and even their location within a snippet, can have a significant influence on the clickthrough of a snippet.
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This paper addresses relevance feedback as an alternative to keyword-based search engines for sifting through large PDF document collections and extracting the most relevant documents (especially for literature review purposes). Until now, relevance feedback has only been used in content-based image and video retrieval due to the inability to query those media types without keywords. Since PDF journal articles contain many valuable non-keyword features such as structure and formatting information as well as embedded figures, they would benefit from relevance feedback. Stripping a PDF into "full-text" for indexing purposes disregards these important features. We discuss how they can be used to our advantage and look to integrate the wealth of knowledge from relevance feedback text-based information retrieval. We argue for the benefits of placing the burden of relevance judgement on the user rather than the retrieval system and present alternative document views that quickly allow the user to deem relevance.
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In information retrieval research, comparing retrieval approaches requires test collections consisting of documents, user requests and relevance assessments. Obtaining relevance assessments that are as sound and complete as possible is crucial for the comparison of retrieval approaches. In XML retrieval, the problem of obtaining sound and complete relevance assessments is further complicated by the structural relationships between retrieval results. A major difference between XML retrieval and flat document retrieval is that the relevance of elements (the retrievable units) is not independent of that of related elements. This has major consequences for the gathering of relevance assessments. This article describes investigations into the creation of sound and complete relevance assessments for the evaluation of content-oriented XML retrieval as carried out at INEX, the evaluation campaign for XML retrieval. The campaign, now in its seventh year, has had three substantially different approaches to gather assessments and has finally settled on a highlighting method for marking relevant passages within documents—even though the objective is to collect assessments at element level. The different methods of gathering assessments at INEX are discussed and contrasted. The highlighting method is shown to be the most reliable of the methods.
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