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Search engine marketing provided by search engines enable companies to promote their products to internet users based on their queries is now a major online advertising channel. In most search-based advertising services, advertisers could have dozens of keywords for the same product or service, and in most instances
Recommender systems have been widely deployed on E-commerce websites. The cold start problem of making effective recommendations to new users without any historical data on the website is still challenging. These new users often have some available information, such as search keywords, before visiting the website. It
Keyword-based search exploits the exact match between the index terms of a query and documents. Thus, some documents, although they are relevant to the given query, may not be returned to users unless the documents include the index terms of the query. Some search engines use the authority of documents, which is
The existing search engines are always lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose. So through analyzing the dynamic search behavior of users, the paper introduces a new method of using a keyword query graph
Traditional search engines simply match according to keywords and recommend information for all the users without considering user preferences. Thus, personalized retrieval technology becomes the ??hotspot?? of current research on information retrieval. On the groundwork of traditional search techniques, this paper
With increasing adoption and presence of Web services, designing novel approaches for efficient Web services recommendation has become steadily more important. Existing Web services discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant Web service search engines, which
keywords that are usually used to describe that topic or category. Additional keywords that the user frequently associates with a topic, such as names of important people, organizations, or a specialized terminology, etc. Are also incorporated into the relevant topic. We use the Apriori Algorithm to extract these associated
Tagging refers to the metadata that many users added in the form of keywords on photos, videos, and other resources for sharing the contents via the internet. However, there are several difficulties with tagging that come from tag variation, tag ambiguity and flat organization. This paper presents the integration of
In Digital Library (DL) system, users interact with the system to search for books or research papers. Users can search through metadata or search for information in the pages by querying using keywords. In both cases, a huge amount of results are returned; however, the relevant ones to the user are not often amongst
information just searching search engine like Google and Baidu with keywords and browseWeb pages selecting useful information, people want to get interested knowledge continuously through pushing technology. Recommendation is of great significance in knowledge discovery. Recommender systems typically produce a list of
Finding relevant and reliable information on the web is a non-trivial task. While internet search engines do find correct web pages with respect to a set of keywords, they often cannot ensure the relevance or reliability of their content. An emerging trend is to harness internet users in the spirit of Web 2.0, to
keywords (descriptive terms), then we modify the ontology accordingly by adding the cluster's terms as semantic terms under the “SubSubSubconcept = lecture” to which these documents belong. This research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University.
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