The Economics of Internet Search

This lecture provides an introduction to the economics of Internet search engines. After a brief review of the historical development of the technology and the industry, I describe some of the economic features of the auction system used for displaying ads. It turns out that some relatively simple economic models provide significant insight into the operation of these auctions. In particular, the classical theory of two-sided matching markets turns out to be very useful in this context.

Is Google the next Microsoft? Competition, Welfare and Regulation in Internet Search

Internet search (or perhaps more accurately `web-search') has grown exponentially over the last decade at an even more rapid rate than the Internet itself. Starting from nothing in the 1990s, today search is a multi-billion dollar business. Search engine providers such as Google and Yahoo! have become household names, and the use of a search engine, like use of the Web, is now a part of everyday life. The rapid growth of online search and its growing centrality to the ecology of the Internet raise a variety of questions for economists to answer. Why is the search engine market so concentrated and will it evolve towards monopoly? What are the implications of this concentration for different `participants' (consumers, search engines, advertisers)? Does the fact that search engines act as `information gatekeepers', determining, in effect, what can be found on the web, mean that search deserves particularly close attention from policy-makers? This paper supplies empirical and theoretical material with which to examine many of these questions. In particular, we (a) show that the already large levels of concentration are likely to continue (b) identify the consequences, negative and positive, of this outcome (c) discuss the possible regulatory interventions that policy-makers could utilize to address these

What Users See – Structures in Search Engine Results Pages

This paper investigates the composition of search engine results pages. We define what elements the most popular web search engines use on their results pages (e.g., organic results, advertisements, shortcuts) and to which degree they are used for popular vs. rare queries. Therefore, we send 500 queries of both types to the major search engines Google, Yahoo, Live.com and Ask. We count how often the different elements are used by the individual engines. In total, our study is based on 42,758 elements. Findings include that search engines use quite different approaches to results pages composition and therefore, the user gets to see quite different results sets depending on the search engine and search query used. Organic results still play the major role in the results pages, but different shortcuts are of some importance, too. Regarding the frequency of certain host within the results sets, we find that all search engines show Wikipedia results quite often, while other hosts shown depend on the search engine used. Both Google and Yahoo prefer results from their own offerings (such as YouTube or Yahoo Answers). Since we used the .com interfaces of the search engines, results may not be valid for other country-specific interfaces

MATCHING DISPLAYED ADS WITH USER QUERIES AND BROWSING BEHAVIOUR TO MEASURE USER SATISFACTION

Online Advertising is said to be the current trend in advertising industry and will be able to survive and focuses on being valuable. For this project, the scope of Online Advertising is going to be discussed. To be more specific, this study will focus mainly from the user perspective. Online Advertising is the act of spreading the message of the products and services over the Internet to targeted potential customers (in this study, the user). The problem from the user perspective is that, the ads retrieved or delivered do not satisfy the user, thus affecting the user browsing behaviour. The objectives are to develop an extension of a normal website to get feedback from the user on the ads retrieved, to evaluate the ads relevancy retrieved based on the user query and browsing behaviour, to prepare a set of questionnaire and to come out with a good policy to measure user satisfaction in browsing for ads. This study will describe how the system is being developed with the support of few literature reviews and findings that had been establish previously. The research methodology approach which is the Rapid Application Prototyping also is being discussed. The four phases involved, together with the project activities are being list down in order to monitor the development of the system. The result and discussion chapter will cover the analysis of the survey that had been conducted. Plus, the study will describe the system architecture and the prototype design of the system

Examining the pseudo-standard web search engine results page

Nearly every web search engine presents its results in an identical format: a ranked list of web page summaries. Each summary comprises a title; some sentence fragments usually containing words used in the query; and URL information about the page. In this study we present data from our pilot experiments with eye tracking equipment to examine how users interact with this standard list of results as presented by the Australian sensis.com.au web search service. In particular, we observe: different behaviours for navigational and informational queries; that users generally scan the list top to bottom; and that eyes rarely wander from the left of the page. We also attempt to correlate the number of bold words (query words) in a summary with the amount of time spent reading the summary. Unfortunately there is no substantial correlation, and so studies relying heavily on this assumption in the literature should be treated with caution

Sequential Selection of Correlated Ads by POMDPs

Online advertising has become a key source of revenue for both web search engines and online publishers. For them, the ability of allocating right ads to right webpages is critical because any mismatched ads would not only harm web users' satisfactions but also lower the ad income. In this paper, we study how online publishers could optimally select ads to maximize their ad incomes over time. The conventional offline, content-based matching between webpages and ads is a fine start but cannot solve the problem completely because good matching does not necessarily lead to good payoff. Moreover, with the limited display impressions, we need to balance the need of selecting ads to learn true ad payoffs (exploration) with that of allocating ads to generate high immediate payoffs based on the current belief (exploitation). In this paper, we address the problem by employing Partially observable Markov decision processes (POMDPs) and discuss how to utilize the correlation of ads to improve the efficiency of the exploration and increase ad incomes in a long run. Our mathematical derivation shows that the belief states of correlated ads can be naturally updated using a formula similar to collaborative filtering. To test our model, a real world ad dataset from a major search engine is collected and categorized. Experimenting over the data, we provide an analyse of the effect of the underlying parameters, and demonstrate that our algorithms significantly outperform other strong baselines