Topic-specific Search Engines Research Articles

Trustworthiness of Review Opinions on the Internet for 3C Commodities

The rapid development of the internet has resulted in rapid e-business growth, with online malls attracting many shoppers due to the privacy and convenience they offer. Like traditional malls, online malls can provide photos, specifications, prices, etc. However, consumers cannot touch the products in reality, which creates risks for the purchase. To date, there has been no research focusing on topic-specific search engines for 3C product reviews based on the trustworthiness of the reviews. This study is the first to sort the reviews of electronic products according to the degree of trust, by analyzing the characteristics of the reviews and the reviewers. This study proposes the criteria for features of the reviews and reviewers to consider to evaluate the trustworthiness of the reviews; builds a search engine to collect the product reviews scattered in opinion websites; and sorts the results by trustworthiness to provide a reliable e-commerce experience. To demonstrate the effectiveness of the proposed method, we conducted a set of experiments, and we adopted the Spearman’s rank correlation coefficient to evaluate the similarity between our method and experts’ opinions. The experimental results showed a high correlation coefficient with the opinions of experts, demonstrating that our method is effective at finding trustworthy reviews on the internet.

Design and evaluation of a web classification system for educational resources

As the web continues to grow, it has become increasingly difficult to retrieve appropriate educational information using traditional search engines. Topic-specific search engines provide an alternative way to support efficient information retrieval in the educational domain. However, developers of topic-specific search engines need to address an issue: how to filter out irrelevant documents from a set of documents collected from the web. This paper proposes a progressive classification approach that combines web content analysis and web structure analysis. In this approach, each web page is represented by a set of content-based and link-based features, which can be used as the input for the classifiers. Three experiments were conducted to compare the proposed progressive approach with two existing web page classification methods. The experimental results showed that the proposed approach in general performed better than the other approaches, and got better precision and recall rates. This proposed approach can be applied in topic-specific search engine development and other web applications such as web content management.

PHYSIOLOGICAL PRINCIPLES MEET INFORMATION EXPLOSION

![Figure][1] J. L. Nation's 2nd edition of `Insect Physiology and Biochemistry', which builds on the 1st edition published in 2002, provides a timely, updated snapshot of modern insect physiology. The book strives to be comprehensive, and the array of topics covered is impressive – from

A machine learning approach to web page filtering using content and structure analysis

As the Web continues to grow, it has become increasingly difficult to search for relevant information using traditional search engines. Topic-specific search engines provide an alternative way to support efficient information retrieval on the Web by providing more precise and customized searching in various domains. However, developers of topic-specific search engines need to address two issues: how to locate relevant documents (URLs) on the Web and how to filter out irrelevant documents from a set of documents collected from the Web. This paper reports our research in addressing the second issue. We propose a machine-learning-based approach that combines Web content analysis and Web structure analysis. We represent each Web page by a set of content-based and link-based features, which can be used as the input for various machine learning algorithms. The proposed approach was implemented using both a feedforward/backpropagation neural network and a support vector machine. Two experiments were designed and conducted to compare the proposed Web-feature approach with two existing Web page filtering methods — a keyword-based approach and a lexicon-based approach. The experimental results showed that the proposed approach in general performed better than the benchmark approaches, especially when the number of training documents was small. The proposed approaches can be applied in topic-specific search engine development and other Web applications such as Web content management.

Query routing for Web search engines: architecture and experiments

General-purpose search engines such as AltaVista and Lycos are notorious for returning irrelevant results in response to user queries. Consequently, thousands of specialized, topic-specific search engines (from VacationSpot.com to KidsHealth.org) have proliferated on the Web. Typically, topic-specific engines return far better results for `on topic' queries as compared with standard Web search engines. However, it is difficult for the casual user to identify the appropriate specialized engine for any given search. It is more natural for a user to issue queries at a particular Web site, and have these queries automatically routed to the appropriate search engine(s). This paper describes an automatic query routing system called Q-Pilot. Q-Pilot has an off-line component that creates an approximate model of each specialized search engine's topic. On line, Q-Pilot attempts to dynamically route each user query to the appropriate specialized search engines. In our experiments, Q-Pilot was able to identify the appropriate query category 70% of the time. In addition, Q-Pilot picked the best search engine for the query, as one of the top three picks out of its repository of 144 engines, about 40% of the time. This paper reports on Q-Pilot's architecture, the query expansion and clustering algorithms it relies on, and the results of our preliminary experiments.