Search results

Most e‐commerce web sites use HTML forms for user authentication, new user registration, newsletter subscription, and searching for products and services. The purpose of this paper is to present a method for automated classification of HTML forms, which is important for search engine applications, e.g. Yahoo Shopping and Google's Froogle, as they can be used to improve the quality of the index and accuracy of search results.

Describes a technique for classifying HTML forms based on their features. Develops algorithms for automatic feature generation of HTML forms and a neural network to classify them.

The authors tested their classifier on an e‐commerce data set and a randomly retrieved data set and achieved accuracy of 94.7 and 93.9 per cent respectively. Experimental results show that the classifier is effective and efficient on both test beds, suggesting that it is a promising general purpose method.

The paper is of value to those involved with information management and e‐commerce.

The existence and continued growth of the invisible web creates a major challenge for search engines that are attempting to organize all of the material on the web into a form that is easily retrieved by all users. The purpose of this paper is to identify the challenges and problems underlying existing work in this area.

A discussion based on a short survey of prior work, including automated discovery of invisible web site search interfaces, automated classification of invisible web sites, label assignment and form filling, information extraction from the resulting pages, learning the query language of the search interface, building content summary for an invisible web site, selecting proper databases, integrating invisible web‐search interfaces, and accessing the performance of an invisible web site.

Existing technologies and tools for indexing the invisible web follow one of two strategies: indexing the web site interface or examining a portion of the contents of an invisible web site and indexing the results.

The paper is of value to those involved with information management.

The purpose of this paper is to analyze how social networking services (SNSs) affect consumers’ behaviors on the omni-channel supply chain by using a reverse research method.

Initially, a questionnaire was administered to obtain data on the relationship between the perception factors of channels and consumer behavior. Subsequently, a structural equation model was constructed, and consumer behavior were determined in the omni-channel supply chain. Finally, the importance of various factors that affected consumer behavior in the omni-channel supply chain under SNSs was determined.

Conclusions affirm that a positive effect on consumer channel behaviors occurs when buyers obtain information from social network platforms. However, regardless of online, offline, or mobile terminal, shortcomings are indicated in consumers’ lack of feedback on purchased goods and the bias of feedback.

The study explored ways to efficiently apply SNSs in building the omni-channel supply chain. Meanwhile, corresponding suggestions were provided such that companies will know about consumer needs.