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.
The lack of user activity on search results was until recently perceived as a sign of user dissatisfaction from retrieval performance, often, referring to such inactivity as a failed search (negative search abandonment). However, recent studies suggest that some search tasks can be achieved in the contents of the results displayed without the need to click through them (positive search abandonment); thus they emphasize the need to discriminate between successful and failed searches without follow-up clicks. In this paper, we study users’ inactivity on search results in relation to their pursued search goals and investigate the impact of displayed results on user clicking decisions. Our study examines two types of post-query user inactivity: pre-determined and post-determined depending on whether the user started searching with a preset intention to look for answers only within the result snippets and did not intend to click through the results, or the user inactivity was decided after the user had reviewed the list of retrieved documents. Our findings indicate that 27 of web searches in our sample are conducted with a pre-determined intention to look for answers in the results’ list and 75 of them can be satisfied in the contents of the displayed results. Moreover, in nearly half the queries that did not yield result visits, the desired information is found in the result snippets. Web search engines have historically focused on connecting people with information resources. For example, if a person wanted to know when their flight to Hyderabad was leaving, a search engine might connect them with the airline where they could find flight status information. However, search engines have recently begun to try to meet people's search needs directly, providing, for example, flight status information in response to queries that include an airline and a flight number. In this paper, we use large scale query log analysis to explore the challenges a search engine faces when trying to meet an information need directly in the search result page. We look at how people's interaction behavior changes when inline content is returned, finding that such content can cannibalize clicks from the algorithmic results. We see that in the absence of interaction behavior, an individual's repeat search behavior can be useful in understanding the content's value. We also discuss some of the ways user behavior can be used to provide insight into when inline answers might better trigger and what types of additional information might be included in the results. It is often considered that high abandonment rate corresponds to poor IR system performance. However several studies suggested that there are so called good abandonments, i.e. situations when search engine result page (SERP) contains enough details to satisfy the user information need without necessity to click on search results. In those papers only editorial metrics of SERP were used, and one cannot be sure that situations marked as good abandonments by assessors actually imply user satisfaction. In present work we propose some real-world evidences for good abandonments by calculating correlation between editorial and click metrics. Query abandonment by search engine users is generally considered to be a negative signal. In this paper, we explore the concept of good abandonment. We define a good abandonment as an abandoned query for which the user's information need was successfully addressed by the search results page, with no need to click on a result or refine the query. We present an analysis of abandoned internet search queries across two modalities (PC and mobile) in three locales. The goal is to approximate the prevalence of good abandonment, and to identify types of information needs that may lead to good abandonment, across different locales and modalities. Our study has three key findings: First, queries potentially indicating good abandonment make up a significant portion of all abandoned queries. Second, the good abandonment rate from mobile search is significantly higher than that from PC search, across all locales tested. Third, classified by type of information need, the major classes of good abandonment vary dramatically by both locale and modality. Our findings imply that it is a mistake to uniformly consider query abandonment as a negative signal. Further, there is a potential opportunity for search engines to drive additional good abandonment, especially for mobile search users, by improving search features and result snippets.