A Comparative Analysis of Web Search Query: Informational Vs. Navigational Queries

The search engines are mainly used to retrieve relevant information. Information retrieval researchers show that queries are the basis for providing better search engine performance. The search query is becoming a means for users to search for their needed information. Web search query is one of the common search queries that is widely used in domain areas. However, the main challenge is the absence of a clear understanding of how web search query influences the users’ behavior on different web search engines. With the emergence of different types of a web search query, the understanding of user behavior on a web search query guides in improving the performance of many web search engines. Current research focused on using informational queries to search relevance information from a database while ignoring the importance of navigational queries. In this paper, we compared the informational and navigational type of a web search query that is mostly used in academic settings. Specifically, we examine the problems, solutions and techniques used in each of these types. We used a query log to conduct an experiment using BM25 mathematical model. The results indicated that the informational search query performed best because several keywords have been included to properly explain the queries. Also, language vocabularies used in informational queries contributed to better search performance. We believed that the outcomes of our comparisons will guide web search engine developers on the right search query for their web search engines

Reply With: Proactive Recommendation of Email Attachments

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 201

Characterizing and Predicting Email Deferral Behavior

Email triage involves going through unhandled emails and deciding what to do with them. This familiar process can become increasingly challenging as the number of unhandled email grows. During a triage session, users commonly defer handling emails that they cannot immediately deal with to later. These deferred emails, are often related to tasks that are postponed until the user has more time or the right information to deal with them. In this paper, through qualitative interviews and a large-scale log analysis, we study when and what enterprise email users tend to defer. We found that users are more likely to defer emails when handling them involves replying, reading carefully, or clicking on links and attachments. We also learned that the decision to defer emails depends on many factors such as user's workload and the importance of the sender. Our qualitative results suggested that deferring is very common, and our quantitative log analysis confirms that 12% of triage sessions and 16% of daily active users had at least one deferred email on weekdays. We also discuss several deferral strategies such as marking emails as unread and flagging that are reported by our interviewees, and illustrate how such patterns can be also observed in user logs. Inspired by the characteristics of deferred emails and contextual factors involved in deciding if an email should be deferred, we train a classifier for predicting whether a recently triaged email is actually deferred. Our experimental results suggests that deferral can be classified with modest effectiveness. Overall, our work provides novel insights about how users handle their emails and how deferral can be modeled

Learning with Weak Supervision for Email Intent Detection

Email remains one of the most frequently used means of online communication. People spend a significant amount of time every day on emails to exchange information, manage tasks and schedule events. Previous work has studied different ways for improving email productivity by prioritizing emails, suggesting automatic replies or identifying intents to recommend appropriate actions. The problem has been mostly posed as a supervised learning problem where models of different complexities were proposed to classify an email message into a predefined taxonomy of intents or classes. The need for labeled data has always been one of the largest bottlenecks in training supervised models. This is especially the case for many real-world tasks, such as email intent classification, where large scale annotated examples are either hard to acquire or unavailable due to privacy or data access constraints. Email users often take actions in response to intents expressed in an email (e.g., setting up a meeting in response to an email with a scheduling request). Such actions can be inferred from user interaction logs. In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails. We develop an end-to-end robust deep neural network model for email intent identification that leverages both clean annotated data and noisy weak supervision along with a self-paced learning mechanism. Extensive experiments on three different intent detection tasks show that our approach can effectively leverage the weakly supervised data to improve intent detection in emails.Comment: 10 pages, 3 figure

Terms interrelationship query expansion to improve accuracy of Quran search

Quran retrieval system is becoming an instrument for users to search for needed information. The search engine is one of the most popular search engines that successfully implemented for searching relevant verses queries. However, a major challenge to the Quran search engine is word ambiguities, specifically lexical ambiguities. With the advent of query expansion techniques for Quran retrieval systems, the performance of the Quran retrieval system has problem and issue in terms of retrieving users needed information. The results of the current semantic techniques still lack precision values without considering several semantic dictionaries. Therefore, this study proposes a stemmed terms interrelationship query expansion approach to improve Quran search results. More specifically, related terms were collected from different semantic dictionaries and then utilize to get roots of words using a stemming algorithm. To assess the performance of the stemmed terms interrelationship query expansion, experiments were conducted using eight Quran datasets from the Tanzil website. Overall, the results indicate that the stemmed terms interrelationship query expansion is superior to unstemmed terms interrelationship query expansion in Mean Average Precision with Yusuf Ali 68%, Sarawar 67%, Arberry 72%, Malay 65%, Hausa 62%, Urdu 62%, Modern Arabic 60% and Classical Arabic 59%

ANALYZING USER TRADEOFFS FOR ENCRYPTED EMAIL SERVICES

Securing online communication, especially in email settings, is challenging. End-to-end encryption achieves maximal security; however, introducing search capabilities is complicated, potentially making it impractical for email. One option is to locally decrypt and index emails to incorporate search, but this requires significant client-side storage. Encryption that is searchable at the server-side limits local storage, but requires other compromises as well. This thesis presents a study using conjoint analysis to understand user tradeoffs related to email features in order to propose a solution for providing usable, yet secure, email service. The results suggest that while it is ideal to have maximum privacy, users rely heavily on the features present in standard insecure email services. Furthermore, with about half of the participants reporting local device storage as a concern, searchable encryption could be a feasible secure email service solution for some users

Users, Queries, and Bad Abandonment in Web Search

After a user submits a query and receives a list of search results, the user may abandon their query without clicking on any of the search results. A bad query abandonment is when a searcher abandons the SERP because they were dissatisfied with the quality of the search results, often making the user reformulate their query in the hope of receiving better search results. As we move closer to understanding when and what causes a user to abandon their query under different qualities of search results, we move forward in an overall understanding of user behavior with search engines. In this thesis, we describe three user studies to investigate bad query abandonment. First, we report on a study to investigate the rate and time at which users abandon their queries at different levels of search quality. We had users search for answers to questions, but showed users manipulated SERPs that contain one relevant document placed at different ranks. We show that as the quality of search results decreases, the probability of abandonment increases, and that users quickly decide to abandon their queries. Users make their decisions fast, but not all users are the same. We show that there appear to be two types of users that behave differently, with one group more likely to abandon their query and are quicker in finding answers than the group less likely to abandon their query. Second, we describe an eye-tracking experiment that focuses on understanding possible causes of users' willingness to examine SERPs and what motivates users to continue or discontinue their examination. Using eye-tracking data, we found that a user deciding to abandon a query is best understood by the user's examination pattern not including a relevant search result. If a user sees a relevant result, they are very likely to click it. However, users' examination of results are different and may be influenced by other factors. The key factors we found are the rank of search results, the user type, and the query quality. For example, we show that regardless of where the relevant document is placed in the SERP, the type of query submitted affects examination, and if a user enters an ambiguous query, they are likely to examine fewer results. Third, we show how the nature of non-relevant material affects users' willingness to further explore a ranked list of search results. We constructed and showed participants manipulated SERPs with different types of non-relevant documents. We found that user examination of search results and time to query abandonment is influenced by the coherence and type of non-relevant documents included in the SERP. For SERPs coherent on off-topic results, users spend the least amount of time before abandoning and are less likely to request to view more results. The time they spend increases as the SERP quality improves, and users are more likely to request to view more results when the SERP contains diversified non-relevant results on multiple subtopics