A Systematic Review on Search Engine Advertising

The innovation of Search Engine Advertising (SEA) was first introduced in 1998. It soon became a very popular tool among practitioners for promoting their websites on the Web and turned into a billion dollar revenue source for search engines. In parallel with its rapid growth in use, SEA attracted the attention of academic researchers resulting in a large number of publications on the topic of SEA. However, no comprehensive review of this accumulated body of knowledge is currently available. This shortcoming has motivated us to conduct a systematic review of SEA literature. Herewith, we searched for and collected 101 papers on the topic of SEA, published in 72 journals from different disciplines and analyzed them to answer the research questions for this study. We have identified the historical development of SEA literature, predominant journals in the publication of SEA research, active reference disciplines as well as the main researchers in the field of SEA. Moreover, we have classified SEA literature into four categories and 10 research topics. We also uncovered a number of gaps in SEA literature and provided future research direction accordingly. Available at: https://aisel.aisnet.org/pajais/vol7/iss3/2

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

A usability approach to improving the user experience in web directories

PhDWeb directories are hierarchically organised website collections that offer users subjectbased access to the Web. They played a significant part in navigating the Web in the past but their role has been weakened in recent years due to their cumbersome expanding collections. This thesis presents a unified framework combining the advantages of personalisation and redefined directory search for improving the usability of Web directories. The thesis begins with an examination of classification schemes that identifies the rigidity of hierarchical classifications and their suitability for Web directories in contrast to faceted classifications. This leads on to an Ontological Sketch Modelling (OSM) case study which identifies the misfits affecting user navigation in Web directories from known rigidity issues. The thesis continues with a review of personalisation techniques and a discussion of the user search model of Web directories following the suggested directions of improvement from the case study. A proposed user-centred framework to improve the usability of Web directories which consists of an individual content-based personalisation model and a redefined search model is then implemented as D-Persona and D-Search respectively. The remainder of the thesis is concerned with a usability test of D-Persona and D-Search aimed at discovering the efficiency, effectiveness and user satisfaction of the solution. This involves an experimental design, test results and discussions for the comparative user study. This thesis extracts a formal definition of the rigidity of hierarchies from their characteristics and justifies why hierarchies are still better suited than facets in organising Web directories. Second, it identifies misfits causing poor usability in Web directories based on the discovered rigidity of hierarchies. Third, it proposes a solution to tackle the misfits and improve the usability of Web directories which has been experimentally proved to be successful

The expected metric principle for probabilistic information retrieval

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (leaves 125-128).Traditionally, information retrieval systems aim to maximize the number of relevant documents returned to a user within some window of the top. For that goal, the Probability Ranking Principle, which ranks documents in decreasing order of probability of relevance, is provably optimal. However, there are many scenarios in which that ranking does not optimize for the user's information need. One example is when the user would be satisfied with some limited number of relevant documents, rather than needing all relevant documents. We show that in such a scenario, an attempt to return many relevant documents can actually reduce the chances of finding any relevant documents. In this thesis, we introduce the Expected Metric Principle, which generalizes the Probability Ranking Principle in a way that intimately connects the evaluation metric and the retrieval model. We observe that given a probabilistic model of relevance, it is appropriate to rank so as to directly optimize these metrics in expectation.(cont.) We consider a number of metrics from the literature, such as the rank of the first relevant result, the %no metric that penalizes a system only for retrieving no relevant results near the top, and the diversity of retrieved results when queries have multiple interpretations, as well as introducing our own new metrics. While direct optimization of a metric's expected value may be computationally intractable, we explore heuristic search approaches, and show that a simple approximate greedy optimization algorithm produces rankings for TREC queries that outperform the standard approach based on the probability ranking principle.by Harr Chen.S.M