Web Retrieval: The Role of Users

摘要

Web retrieval methods have evolved through three major steps in the last decade or so. They started from standard document-centric IR in the early days of the Web, then made a major step forward by leveraging the structure of the Web, using link analysis techniques in both crawling and ranking challenges. A more recent, no less important but maybe more discrete step forward, has been to enter the user in this equation in two ways: (1) implicitly, through the analysis of usage data captured by query logs, and session and click information in general, the goal being to improve ranking as well as to measure user's happiness and engagement; (2) explicitly, by offering novel interactive features; the goal here being to better answer users' needs. In this tutorial, we will cover the user-related challenges associated with the implicit and explicit role of users in Web retrieval. We will review and discuss challenges associated with two types of activities, namely: 1. Usage data analysis and metrics - It is critical to monitor how users interact with Web retrieval systems, as this implicit relevant feedback aggregated at a large scale can approximate quite accurately the level of success of a given feature. Here we have to consider not only clicks statistics but also the time spent in a page, the number of actions per session, etc. 2. User interaction - Given the intrinsic problems posed by the Web, the key challenge for the user is to conceive a good query, one that leads to a manageable and relevant answer. The retrieval system must complete search requests fast and give back relevant results, even for poorly formulated queries. Web retrieval engines thus interact with the user at two key stages, each associated with its own challenges: (1) Expressing a query: Human beings have needs or tasks to accomplish, which are frequently not easy to express as "queries". Queries are just a reflection of human needs and are thus, by definition, imperfect. The issue here is for the engine both to assist the user in reflecting this need and to capture it accurately even if the information is incomplete or poorly expressed. (2) Interpreting results: Even if the user is able to perfectly express a query, the answer might be split over thousands or millions of Web pages or not exist at all. In this context, numerous questions need to be addressed. Examples include: How do we handle a large answer? How do we rank results? How do we select the documents that really are of interest to the user? Even in the case of a single document candidate, the document itself could be large. How do we browse such documents efficiently? The goal of this tutorial is to teach the key principles and technologies behind the activities and challenges briefly outlined above, bring new understanding and insights to the attendees, and hopefully foster future research.