Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

Intent Models for Contextualising and Diversifying Query Suggestions

The query suggestion or auto-completion mechanisms help users to type less while interacting with a search engine. A basic approach that ranks suggestions according to their frequency in the query logs is suboptimal. Firstly, many candidate queries with the same prefix can be removed as redundant. Secondly, the suggestions can also be personalised based on the user's context. These two directions to improve the aforementioned mechanisms' quality can be in opposition: while the latter aims to promote suggestions that address search intents that a user is likely to have, the former aims to diversify the suggestions to cover as many intents as possible. We introduce a contextualisation framework that utilises a short-term context using the user's behaviour within the current search session, such as the previous query, the documents examined, and the candidate query suggestions that the user has discarded. This short-term context is used to contextualise and diversify the ranking of query suggestions, by modelling the user's information need as a mixture of intent-specific user models. The evaluation is performed offline on a set of approximately 1.0M test user sessions. Our results suggest that the proposed approach significantly improves query suggestions compared to the baseline approach.Comment: A short version of this paper was presented at CIKM 201

Bootstrapping Conversational Agents With Weak Supervision

Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called \textit{search, label, and propagate} (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.Comment: 6 pages, 3 figures, 1 table, Accepted for publication in IAAI 201

MINING ACTIONABLE INTENTS IN QUERY ENTITIES

Understanding search engine users’ intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users’ future actions. In this paper, we present a novel research for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, i.e. the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment based on the Action Mining (AM) query entity dataset from Actionable Knowledge Graph (AKG) task at NTCIR-13 suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users

Why people search for images using web search engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1) Why do people search for images in text-based Web image search systems? (2) How does image search behavior

Using Information Filtering in Web Data Mining Process

Web service-oriented Grid is becoming a standard for achieving loosely coupled distributed computing. Grid services could easily be specified with web-service based interfaces. In this paper we first envisage a realistic Grid market with players such as end-users, brokers and service providers participating co-operatively with an aim to meet requirements and earn profit. End-users wish to use functionality of Grid services by paying the minimum possible price or price confined within a specified budget, brokers aim to maximise profit whilst establishing a SLA (Service Level Agreement) and satisfying end-user needs and at the same time resisting the volatility of service execution time and availability. Service providers aim to develop price models based on end-user or broker demands that will maximise their profit. In this paper we focus on developing stochastic approaches to end-user workflow scheduling that provides QoS guarantees by establishing a SLA. We also develop a novel 2-stage stochastic programming technique that aims at establishing a SLA with end-users regarding satisfying their workflow QoS requirements. We develop a scheduling (workload allocation) technique based on linear programming that embeds the negotiated workflow QoS into the program and model Grid services as generalised queues. This technique is shown to outperform existing scheduling techniques that don't rely on real-time performance information