The impact of query suggestion in E-commerce websites

Parallel Sessions: Session 3A (5F) - Online Virtual Worlds and E-Commerce WebsitesTheme: 'E-Life: Web-enabled Convergence of Commerce, Work, and Social Life'In this paper we propose a research agenda for studying the impact of query suggestion features on cognitive load and customer satisfaction during online shopping in e-commerce websites. Despite the popular use of query suggestion features in search engines and large e-commerce websites such as Amazon.com and eBay, there is little research in this area. Based on a review on prior literature in query suggestion and online shopping, a research model and five hypotheses are posed. A lab experiment is proposed to test the hypotheses and potential implications of the research are discussed.postprintThe 10th Annual Workshop on E-Business (Web 2011), Shanghai, China, 4 December 2011

'A Modern Up-To-Date Laptop' -- Vagueness in Natural Language Queries for Product Search

With the rise of voice assistants and an increase in mobile search usage, natural language has become an important query language. So far, most of the current systems are not able to process these queries because of the vagueness and ambiguity in natural language. Users have adapted their query formulation to what they think the search engine is capable of, which adds to their cognitive burden. With our research, we contribute to the design of interactive search systems by investigating the genuine information need in a product search scenario. In a crowd-sourcing experiment, we collected 132 information needs in natural language. We examine the vagueness of the formulations and their match to retailer-generated content and user-generated product reviews. Our findings reveal high variance on the level of vagueness and the potential of user reviews as a source for supporting users with rather vague search intents

Query Log Mining to Enhance User Experience in Search Engines

The Web is the biggest repository of documents humans have ever built. Even more, it is increasingly growing in size every day. Users rely on Web search engines (WSEs) for finding information on the Web. By submitting a textual query expressing their information need, WSE users obtain a list of documents that are highly relevant to the query. Moreover, WSEs tend to store such huge amount of users activities in "query logs". Query log mining is the set of techniques aiming at extracting valuable knowledge from query logs. This knowledge represents one of the most used ways of enhancing the users’ search experience. According to this vision, in this thesis we firstly prove that the knowledge extracted from query logs suffer aging effects and we thus propose a solution to this phenomenon. Secondly, we propose new algorithms for query recommendation that overcome the aging problem. Moreover, we study new query recommendation techniques for efficiently producing recommendations for rare queries. Finally, we study the problem of diversifying Web search engine results. We define a methodology based on the knowledge derived from query logs for detecting when and how query results need to be diversified and we develop an efficient algorithm for diversifying search results