Desktop search

About: Desktop search is a research topic. Over the lifetime, 208 publications have been published within this topic receiving 2995 citations.

Universal knowledge management and desktop search system

Abstract: A system (and method) for device-independent point to multipoint communication is disclosed The system is configured to receive a message addressed to one or more destination users, the message type being, for example, Short Message Service (SMS), Instant Messaging (IM), E-mail, web form input, or Application Program Interface (API) function call The system also is configured to determine information about the destination users, the information comprising preferred devices and interfaces for receiving messages, the information further comprising message receiving preferences The system applies rules to the message based on destination user information to determine the message endpoints, the message endpoints being, for example, Short Message Service (SMS), Instant Messaging (IM), E-mail, web page output, or Application Program Interface (API) function call The system translates the message based on the destination user information and message endpoints and transmits the message to each endpoint of the message A system (and method) also enables user to perform a keyword search, after performing the keyword search, a result display on the first computing device to the first user, in which are displayed individual messages located in the keyword search, wherein the result display provides a graphical subscribe indicator that, when selected after the search is performed and after the individual messages are displayed, subscribes the first user to a second user who provided the selected individual message from the search results, so as to enable the first user to be a follower of the second user, wherein the first user becomes one of several followers of the second user and the second user has a second computing device and storing, by the one or more computer processors, the followers of the second user, including the first user, in a first storage User is also enabling to post, a new message for distribution to one or more unspecified recipients wherein server identifies the followers of the posting user as recipients of the new message and sending the new message to the followers of the second user, including the first user

Query suggestions in the absence of query logs

Abstract: After an end-user has partially input a query, intelligent search engines can suggest possible completions of the partial query to help end-users quickly express their information needs. All major web-search engines and most proposed methods that suggest queries rely on search engine query logs to determine possible query suggestions. However, for customized search systems in the enterprise domain, intranet search, or personalized search such as email or desktop search or for infrequent queries, query logs are either not available or the user base and the number of past user queries is too small to learn appropriate models. We propose a probabilistic mechanism for generating query suggestions from the corpus without using query logs. We utilize the document corpus to extract a set of candidate phrases. As soon as a user starts typing a query, phrases that are highly correlated with the partial user query are selected as completions of the partial query and are offered as query suggestions. Our proposed approach is tested on a variety of datasets and is compared with state-of-the-art approaches. The experimental results clearly demonstrate the effectiveness of our approach in suggesting queries with higher quality.

Improved search engines and navigation preference in personal information management

Abstract: Traditionally users access their personal files mainly by using folder navigation. We evaluate whether recent improvements in desktop search have changed this fundamental aspect of Personal Information Management (PIM). We tested this in two studies using the same questionnaire: (a) The Windows Studya longitudinal comparison of Google Desktop and Windows XP Search Companion, and (b) The Mac Studya large scale comparison of Mac Spotlight and Sherlock. There were few effects for improved search. First, regardless of search engine, there was a strong navigation preference: on average, users estimated that they used navigation for 56-68% of file retrieval events but searched for only 4-15% of events. Second, the effect of improving the quality of the search engine on search usage was limited and inconsistent. Third, search was used mainly as a last resort when users could not remember file location. Finally, there was no evidence that using improved desktop search engines leads people to change their filing habits to become less reliant on hierarchical file organization. We conclude by offering theoretical explanations for navigation preference, relating to differences between PIM and Internet retrieval, and suggest alternative design directions for PIM systems.

Interactive information retrieval

Abstract: Information retrieval is a fundamental component of human information behavior. The ability to extract useful information from large electronic resources not only is one of the main activities of individuals online but is an essential skill for most professional groups and a means of achieving competitive advantage. Our electronic information world is becoming increasingly complex with more sources of information, types of information, and ways to access information than ever before. Anyone who searches for information is required to make more decisions about searching and expected to engage with an increased number and variety of search systems. Even a standard desktop personal computer comes equipped with numerous search tools (desktop search, e-mail search, browsers to help search the Internet, embedded search tools for specific file formats such as PDF [portable document format] or Word, and specific document types such as help manuals). A standard day, if one is electronically enabled, may involve many searches across different search systems accessing different electronic resources for different purposes. The Internet, in particular, has revolutionized the ability to search, especially in the commercial arena where we have the choice of using different search systems to search essentially the same electronic resources but with different interactive functionalities. The search decisions a human is required to make before encountering any information involve not only how to search this resource using this system but also how to choose a system or resource to search in the first place. These decisions are complicated because skills learned using one type of system do not always transfer simply to searching a different type of system (Cool, Park, Belkin, Koenemann, & Ng, 1996). Neither does information literacy in one domain of expertise necessarily help when searching on unfamiliar topics. The variability of data available, and the explicit or implicit structures of the data, also place a burden on both the searchers and system designers. How does searching within a Weblog, for example, differ from searching within technical manuals; or does all searching involve the same activities and require the same user support? As research shows CHAPTER 2

Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance

Abstract: In this paper, we present a log-based study on user search behavior comparisons on three different platforms: desktop, mobile and tablet. We use three-month search logs in 2012 from a commercial search engine for our study. Our objective is to better understand how and to what extent mobile and tablet searchers behave differently than desktop users. Our study spans a variety of aspects including query categorization, query length, search time distribution, search location distribution, user click patterns and so on. From our data set, we reveal that there are significant differences between user search patterns in these three platforms, and therefore use the same ranking system is not an optimal solution for all of them. Consequently, we propose a framework that leverages a set of domain-specific features, along with the training data from desktop search, to further improve the search relevance for mobile and tablet platforms. Experimental results demonstrate that by transferring knowledge from desktop search, search relevance on mobile and tablet can be greatly improved.