Query Chains: Learning to Rank from Implicit Feedback

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.Comment: 10 page

Shuffling a Stacked Deck: The Case for Partially Randomized Ranking of Search Engine Results

In-degree, PageRank, number of visits and other measures of Web page popularity significantly influence the ranking of search results by modern search engines. The assumption is that popularity is closely correlated with quality, a more elusive concept that is difficult to measure directly. Unfortunately, the correlation between popularity and quality is very weak for newly-created pages that have yet to receive many visits and/or in-links. Worse, since discovery of new content is largely done by querying search engines, and because users usually focus their attention on the top few results, newly-created but high-quality pages are effectively ``shut out,'' and it can take a very long time before they become popular. We propose a simple and elegant solution to this problem: the introduction of a controlled amount of randomness into search result ranking methods. Doing so offers new pages a chance to prove their worth, although clearly using too much randomness will degrade result quality and annul any benefits achieved. Hence there is a tradeoff between exploration to estimate the quality of new pages and exploitation of pages already known to be of high quality. We study this tradeoff both analytically and via simulation, in the context of an economic objective function based on aggregate result quality amortized over time. We show that a modest amount of randomness leads to improved search results

Theory-based user modeling for personalized interactive information retrieval

In an effort to improve users’ search experiences during their information seeking process, providing a personalized information retrieval system is proposed to be one of the effective approaches. To personalize the search systems requires a good understanding of the users. User modeling has been approved to be a good method for learning and representing users. Therefore many user modeling studies have been carried out and some user models have been developed. The majority of the user modeling studies applies inductive approach, and only small number of studies employs deductive approach. In this paper, an EISE (Extended Information goal, Search strategy and Evaluation threshold) user model is proposed, which uses the deductive approach based on psychology theories and an existing user model. Ten users’ interactive search log obtained from the real search engine is applied to validate the proposed user model. The preliminary validation results show that the EISE model can be applied to identify different types of users. The search preferences of the different user types can be applied to inform interactive search system design and development

Search engine ranking factors analysis : Moz digital marketing company survey study

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe use of the Internet increases every year in the world for multiple purposes and at significant rates. In the same way, access to electronic business and personal pages allowing commercial transactions follows these high evolution rates. Many studies on this subject have pointed that it is important for most businesses to have a web presence. The key to be found by the right product or service target audience, at the right moment, according to most of authors, lies with search engines (SE) advent. However, there had been frequently changes in search engines ranking website classification algorithms during the last years. To accomplish this model evolution, the Search Engine Optimization (SEO) professionals must to frequently adopt to constant changes regarding ranking classification strategies from SE schemas of work. In this work the author explored a wide range of factors that may influence search engine result pages (SERP’s) and examined recent aspects of user experience over a website that are increasing importance regarding the optimization to be done over the web pages, internal and external page links, and its technical components. In addition, it seems that the user action and involvement over the website are key factors that Google will probably continue to adopt to determine websites rank in SERP’s. As an empirical study, all efforts to discover the SE website promotion ranking factors are based on trial and error activities and there is no official knowledge base regarding these protected secrets kept by the major players of this valuable market. Due to the lack of published academic research works in this area, the present work has discovered and documented SE ranking factors based on survey data by a large quantity of companies in digital marketing segment. At the end of the project the author intends to present the state-of-the-art in this field of study as well as some market perception evolution of this subject based heavily on practical experiments and most recent literature in this area. Moreover, it is growing the debate about the limits of digital marketing. Due the powerful influence of SE to market and people behavior, the presented study data and considerations raise an important forum of discussion now and in the future concerning ethics and socially acceptable limits and controls over personal information on the internet

BlogForever D5.2: Implementation of Case Studies

This document presents the internal and external testing results for the BlogForever case studies. The evaluation of the BlogForever implementation process is tabulated under the most relevant themes and aspects obtained within the testing processes. The case studies provide relevant feedback for the sustainability of the platform in terms of potential users’ needs and relevant information on the possible long term impact