Paid Placement: Advertising and Search on the Internet

Paid placement, where advertisers bid payments to a search engine to have their products appear next to keyword search results, has emerged as a predominant form of advertising on the Internet. This paper studies a product-di¤erentiation model where consumers are initially uncertain about the desirability of and valuation for di¤erent sellers products, and can learn about a seller s product through a costly search. In equilibrium, a seller bids more for placement when his product is more relevant for a given keyword, and the paid placement of sellers by the search engine reveals information about the relevance of their products. This results in e¢ cient (sequential) search by consumers and increases total output

A New Web Search Engine with Learning Hierarchy

Most of the existing web search engines (such as Google and Bing) are in the form of keyword-based search. Typically, after the user issues a query with the keywords, the search engine will return a flat list of results. When the query issued by the user is related to a topic, only the keyword matching may not accurately retrieve the whole set of webpages in that topic. On the other hand, there exists another type of search system, particularly in e-Commerce web- sites, where the user can search in the categories of different faceted hierarchies (e.g., product types and price ranges). Is it possible to integrate the two types of search systems and build a web search engine with a topic hierarchy? The main diffculty is how to classify the vast number of webpages on the Internet into the topic hierarchy. In this thesis, we will leverage machine learning techniques to automatically classify webpages into the categories in our hierarchy, and then utilize the classification results to build the new search engine SEE. The experimental results demonstrate that SEE can achieve better search results than the traditional keyword-based search engine in most of the queries, particularly when the query is related to a topic. We also conduct a small-scale usability study which further verifies that SEE is a promising search engine. To further improve SEE, we also propose a new active learning framework with several novel strategies for hierarchical classification

Schema-aware keyword search on linked data

Keyword search is a popular technique for querying the ever growing repositories of RDF graph data on the Web. This is due to the fact that the users do not need to master complex query languages (e.g., SQL, SPARQL) and they do not need to know the underlying structure of the data on the Web to compose their queries. Keyword search is simple and flexible. However, it is at the same time ambiguous since a keyword query can be interpreted in different ways. This feature of keyword search poses at least two challenges: (a) identifying relevant results among a multitude of candidate results, and (b) dealing with the performance scalability issue of the query evaluation algorithms. In the literature, multiple schema-unaware approaches are proposed to cope with the above challenges. Some of them identify as relevant results only those candidate results which maintain the keyword instances in close proximity. Other approaches filter out irrelevant results using their structural characteristics or rank and top-k process the retrieved results based on statistical information about the data. In any case, these approaches cannot disambiguate the query to identify the intent of the user and they cannot scale satisfactorily when the size of the data and the number of the query keywords grow. In recent years, different approaches tried to exploit the schema (structural summary) of the RDF (Resource Description Framework) data graph to address the problems above. In this context, an original hierarchical clustering technique is introduced in this dissertation. This approach clusters the results based on a semantic interpretation of the keyword instances and takes advantage of relevance feedback from the user. The clustering hierarchy uses pattern graphs which are structured queries and clustering together result graphs with the same structure. Pattern graphs represent possible interpretations for the keyword query. By navigating though the hierarchy the user can select the pattern graph which is relevant to her intent. Nevertheless, structural summaries are approximate representations of the data and, therefore, might return empty answers or miss results which are relevant to the user intent. To address this issue, a novel approach is presented which combines the use of the structural summary and the user feedback with a relaxation technique for pattern graphs to extract additional results potentially of interest to the user. Query caching and multi-query optimization techniques are leveraged for the efficient evaluation of relaxed pattern graphs. Although the approaches which consider the structural summary of the data graph are promising, they require interaction with the user. It is claimed in this dissertation that without additional information from the user, it is not possible to produce results of high quality from keyword search on RDF data with the existing techniques. In this regard, an original keyword query language on RDF data is introduced which allows the user to convey his intention flexibly and effortlessly by specifying cohesive keyword groups. A cohesive group of keywords in a query indicates that its keywords should form a cohesive unit in the query results. It is experimentally demonstrated that cohesive keyword queries improve the result quality effectively and prune the search space of the pattern graphs efficiently compared to traditional keyword queries. Most importantly, these benefits are achieved while retaining the simplicity and the convenience of traditional keyword search. The last issue addressed in this dissertation is the diversification problem for keyword search on RDF data. The goal of diversification is to trade off relevance and diversity in the results set of a keyword query in order to minimize the dissatisfaction of the average user. Novel metrics are developed for assessing relevance and diversity along with techniques for the generation of a relevant and diversified set of query interpretations for a keyword query on an RDF data graph. Experimental results show the effectiveness of the metrics and the efficiency of the approach

Overview of Social Capital and Financial Cooperative through Bibliometric Visualizing from 1992-2022

Purpose : The purposes of this study to describe in the form of a map of research in the field of Social capital  related to  financial cooperative from international scale publications from the search engine Scopus. Design/methodology/approach: This study uses bibiometric and analyze search results provided by Scopus and by using Vosviewer software to show the keyword groups that can be identified from all relevant research in this study. Findings: There is very little research on social capital related to cooperatives, the results of the search for publications from 1992 to 2022 are only 175 documents.  Originality/value: This paper is original Paper type: Research articl

Aplikasi Model ARIMAX dengan Efek Variasi Kalender untuk Peramalan Trend Pencarian Kata Kunci “Zalora” pada Data Google Trends

ARIMAX is a method in time series analysis that is used to model an event by adding exogenous variables as additional information. Currently, the ARIMAX model can be applied to time series data that has calendar variation effects. In short, calendar variations occur due to changes in the composition of the calendar. The purpose of this study is to apply the ARIMAX model with the effects of calendar variations to forecast search trends for the keyword "Zalora". Data were collected starting from January 2018 to November 2022 in the form of a weekly series. Based on the results of the analysis, the ARIMAX model is obtained with calendar variation effects with ARIMA residuals (1,1,1). Forecasting accuracy using the Mean Absolute Percentage Error (MAPE) of 10.47%. Forecasting results for the next 24 periods tend to fluctuate and it is estimated that in April 2023 there will be an increase in search trends for the keyword "Zalora"

Leave Me My Name! : Why Competitive Keyword Advertising is an Ethical Landmine for Attorneys

Search engine providers, like Google and Yahoo!, make money by allowing advertisers to bid on keywords. When a user enters the keyword, the advertisement is prominently displayed, typically toward the top of the results. States are divided on whether to allow attorneys to bid on the names of other attorneys—a practice known as competitive keyword advertising. On one hand, just this summer, a New Jersey ethics panel took the position that competitive keyword advertising does not, generally, violate of the rules of professional conduct. However, the advertisement may not include text with the searched-for attorney’s name that is hyperlinked to the advertising attorney’s website. Similarly, the Florida Bar Board of Governors recently passed a limited form of regulation, which awaits the Supreme Court of Florida’s approval before taking effect. Texas has held competitive keyword advertising is ethical as long as a reasonable person would not believe the advertising attorney is associated with the searched-for attorney. Kentucky has not taken a stance per se but it did not discipline a lawyer for participating. On the other hand, South and North Carolina have disciplined attorneys for bidding on another attorney’s name. Those in favor of allowing attorneys to bid on other attorneys’ names have argued that a prohibition would effectively create a new intellectual property right in attorney names. They argue competitive keyword advertising does not violate trademark law or publicity rights. This Article argues in favor of regulation. It shows the debate is more complicated than the scholarly literature currently acknowledges and, in particular, questions the way in which intellectual property has been used as a framework. First, a violation of a disciplinary rule does not depend upon a violation of civil law. Neither a violation of trademark law nor publicity rights is necessary for the imposition of discipline. Second, search engine providers, and their customers that purchase keywords, have a pecuniary interest in making keyword advertisements look like organic results: Consumers trust organic results more. Indeed, substantial empirical evidence, in numerous studies, demonstrates consumers struggle to identify which results of a search are advertisements. This kind of confusion should be of concern to regulators because it suggests all keyword advertising (even keyword advertising that does not involve the purchase of another attorney’s name) is manipulative and inherently misleading. Trademark law is not concerned with this kind of confusion: It merely cares about whether consumers are confused as to source, sponsorship, or affiliation. Third, some evidence suggests consumers are confused in the trademark sense: At least when the advertisement uses the searched-for attorney’s name, some clients have actually hired attorneys thinking they were someone else. Even more have probably clicked on a link for one attorney only to realize it was someone else, unaffiliated with the attorney they searched for. This causes frustration and distrust of the legal profession. Furthermore, competitive keyword advertising is a dishonorable attempt by attorneys to piggyback on the reputation of another attorney, implicating the attorney’s oath. Competitive keyword advertising is thus worse than non-competitive keyword advertising. Fourth, the proposition that attorney participation does not violate publicity rights stems from a single case—Habush v. Cannon. This was a case decided by the Wisconsin Court of Appeals—an intermediate appellate court— and the court stated the decision was “a close one.” Furthermore, the case’s reasoning had little to do with consumer confusion, making it of little value for regulators given that they are primarily concerned with protecting the public. In summary, regulation is justified