Search engines make it easy to check facts online, but finding some specific kinds of information sometimes proves to be difficult. We studied the behavioral signals that suggest that a user is having trouble in a search task. First, we ran a lab study with 23 users to gain a preliminary understanding on how users ’ behavior changes when they struggle finding the information they’re looking for. The observations were then tested with 179 participants who all completed an average of 22.3 tasks from a pool of 100 tasks. The large-scale study provided quantitative support for our qualitative observations from the lab study. When having difficulty in finding information, users start to formulate more diverse queries, they use advanced operators more, and they spend a longer time on the search result page as compared to the successful tasks. The results complement the existing body of research focusing on successful search strategies. Author Keywords Web search, search engines, difficult search tasks, search

The success of “infinite-inventory ” retailers such as Amazon.com and Netflix has been ascribed to a “long tail ” phenomenon. To wit, while the majority of their inventory is not in high demand, in aggregate these “worst sellers, ” unavailable at limited-inventory competitors, generate a significant fraction of total revenue. The long tail phenomenon, however, is in principle consistent with two fundamentally different theories. The first, and more popular hypothesis, is that a majority of consumers consistently follow the crowds and only a minority have any interest in niche content; the second hypothesis is that everyone is a bit eccentric, consuming both popular and specialty products. Based on examining extensive data on user preferences for movies, music, Web search, and Web browsing, we find overwhelming support for the latter theory. However, the observed eccentricity is much less than what is predicted by a fully random model whereby every consumer makes his product choices independently and proportional to product popularity; so consumers do indeed exhibit at least some a priori propensity toward either the popular or the exotic. Our findings thus suggest an additional factor in the success of infinite-inventory retailers, namely, that tail availability may boost head sales by offering consumers the convenience of “one-stop shopping ” for both their mainstream and niche interests. This hypothesis is further supported by our theoretical analysis that presents a simple model in which shared inventory stores, such as Amazon Marketplace, gain a clear advantage by satisfying tail demand, helping to explain the emergence and increasing popularity of such retail arrangements. Hence, we believe that the return-oninvestment (ROI) of niche products goes beyond direct revenue, extending to second-order gains associated with increased consumer satisfaction and repeat patronage. More generally, our findings call into question the conventional wisdom that specialty products only appeal to a minority of consumers.

Search engine switching describes the voluntarily transition from one Web search engine to another. In this paper we present a study of search engine switching behavior that combines largescale log-based analysis and survey data. We characterize aspects of switching behavior, and develop and evaluate predictive models of switching behavior using features of the active query, the current session, and user search history. Our findings provide insight into the decision-making processes of search engine users and demonstrate the relationship between switching and factors such as dissatisfaction with the quality of the results, the desire for broader topic coverage or verification of encountered information, and user preferences. The findings also reveal sufficient consistency in users ’ search behavior prior to engine switching to afford accurate prediction of switching events. Predictive models may be useful for search engines who may want to modify the search experience if they can accurately anticipate a switch.

We investigate how people interact with Web search engine result pages using eye-tracking. While previous research has focused on the visual attention devoted to the 10 organic search results, this paper examines other components of contemporary search engines, such as ads and related searches. We systematically varied the type of task (informational or navigational), the quality of the ads (relevant or irrelevant to the query), and the sequence in which ads of different quality were presented. We measured the effects of these variables on the distribution of visual attention and on task performance. Our results show significant effects of each variable. The amount of visual attention that people devote to organic results depends on both task type and ad quality. The amount of visual attention that people devote to ads depends on their quality, but not the type of task. Interestingly, the sequence and predictability of ad quality is also an important factor in determining how much people attend to ads. When the quality of ads varied randomly from task to task, people paid little attention to the ads, even when they were good. These results further our understanding of how attention devoted to search results is influenced by other page elements, and how previous search experiences influence how people attend to the current page.

In this paper, we present SnipSuggest, a system that provides onthe-go, context-aware assistance in the SQL composition process. SnipSuggest aims to help the increasing population of non-expert database users, who need to perform complex analysis on their large-scale datasets, but have difficulty writing SQL queries. As a user types a query, SnipSuggest recommends possible additions to various clauses in the query using relevant snippets collected from a log of past queries. SnipSuggest’s current capabilities include suggesting tables, views, and table-valued functions in the FROM clause, columns in the SELECT clause, predicates in the WHERE clause, columns in the GROUP BY clause, aggregates, and some support for sub-queries. SnipSuggest adjusts its recommendations according to the context: as the user writes more of the query, it is able to provide more accurate suggestions. We evaluate SnipSuggest over two query logs: one from an undergraduate database class and another from the Sloan Digital Sky Survey database. We show that SnipSuggest is able to recommend useful snippets with up to 93.7 % average precision, at interactive speed. We also show that SnipSuggest outperforms naïve approaches, such as recommending popular snippets. 1.

student.tugraz.at The degree to which users ’ make their search intent explicit can be assumed to represent an upper bound on the level of service that search engines can provide. In a departure from traditional query expansion mechanisms, we introduce Intentional Query Suggestion as a novel idea that is attempting to make users ’ intent more explicit during search. In this paper, we present a prototypical algorithm for Intentional Query Suggestion and we discuss corresponding data from comparative experiments with traditional query suggestion mechanisms. Our preliminary results indicate that intentional query suggestions 1) diversify search result sets (i.e. it reduces result set overlap) and 2) have the potential to yield higher click-through rates than traditional query suggestions.

Commerce search engines have become popular in recent years, as users increasingly search for (and buy) products on the web. In response to an user query, they surface links to products in their catalog (or index) that match the requirements specified in the query. Often, few or no product in the catalog matches the user query exactly, and the search engine is forced to return a set of products that partially match the query. This paper considers the problem of choosing a set of products in response to an user query, so as to ensure maximum user satisfaction. We call this the result enrichment problem in commerce search. The challenge in result enrichment is two-fold: the search engine needs to estimate the extent to which a user genuinely cares about an attribute that she has specified in a query; then, it must display products in the catalog that match the user requirement on the important attributes, but have a similar but possibly non-identical value on the less important ones. To this end, we propose a technique for measuring the importance of individual attribute values and the similarity between different values of an attribute. A novelty of our approach is that we use entire browse trails, rather than just clickthrough rates, in this estimation algorithm. We develop a model for this problem, design and (theoretically) analyze our algorithm for solving it using browse trails, and support our theoretical findings by showing, via experiments conducted on actual user data, that the algorithm performs well in practice. In the course of developing our algorithm, we offer a solution to another problem that might be of independent interest: we give an algorithm for the annotation of web domains by a set of keywords that represent the contents of the domain. 1.

Three methods are proposed to classify queries by intent (CQI), e.g., navigational, informational, commercial, etc. Following mixed-initiative dialog systems, search engines should distinguish navigational queries where the user is taking the initiative from other queries where there are more opportunities for system initiatives (e.g., suggestions, ads). The query intent problem has a number of useful applications for search engines, affecting how many (if any) advertisements to display, which results to return, and how to arrange the results page. Click logs are used as a substitute for annotation. Clicks on ads are evidence for commercial intent; other types of clicks are evidence for other intents. We start with a simple Naïve Bayes baseline that works well when there is plenty of training data. When training data is less plentiful, we back off to nearby URLs in a click graph, using a method similar to Word-Sense Disambiguation. Thus, we can infer that designer trench is commercial because it is close to www.saksfifthavenue.com, which is known to be commercial. The baseline method was designed for precision and the backoff method was designed for recall. Both methods are fast and do not require crawling webpages. We recommend a third method, a hybrid of the two, that does no harm when there is plenty of training data, and generalizes better when there isn’t, as a strong baseline for the CQI task. 1 Classify Queries By Intent (CQI) Determining query intent is an important problem for today’s search engines. Queries are short (consisting of 2.2 terms on average (Beitzel et al., 2004)) and contain ambiguous terms. Search engines need to derive what users want from this limited source of information. Users may be searching for a specific page, browsing for information, or trying to buy something. Guessing the correct intent is important for returning relevant items. Someone searching for designer trench is likely to be interested in results or ads for trench coats, while someone searching for world war I trench might be irritated by irrelevant clothing advertisements.

While current search engines serve known-item search such as homepage finding very well, they generally cannot support exploratory search effectively. In exploratory search, users do not know their information needs precisely and also often lack the needed knowledge to formulate effective queries, thus querying alone, as supported by the current search engines, is insufficient, and browsing into related information would be very useful. Currently, browsing is mostly done by following hyperlinks embedded on Web pages. In this paper, we propose to leverage search logs to allow a user to browse beyond hyperlinks with a multi-resolution topic map constructed based on search logs. Specifically, we treat search logs as “footprints ” left by previous users in the information space and build a multi-resolution topic map to semantically capture and organize them in multiple granularities. Such a topic map can support a user to zoom in, zoom out, and navigate horizontally over the information space, and thus provide flexible and effective browsing capabilities for end users. To test the effectiveness of the proposed methods of supporting browsing, we rely on real search logs and a commercial search engine to implement our proposed methods. Our experimental results show that the proposed topic map is effective to support browsing beyond hyperlinks.

Faceted search systems help people find what they are looking by allowing them to specify not just keywords related to their information need, but also metadata. While such systems hold great potential and have been successfully used in vertical domains, there are many challenges in extending them to large, heterogeneous collections like the Web, corporate intranets, or federated search engines that access many different data silos. In this position paper we discuss the challenges in greater detail. Those that we have identified stem from the fact that such datasets are 1) very large, making it difficult to assign quality meta-data to every document and to retrieve the full set of results and associated metadata at query time, and 2) heterogeneous, making it difficult to apply the same metadata to every result or every query.