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In general, content-based recommender systems use a keyword vector to locate recommendations. However, this method does not consider relations of each keyword and it is also inscrutable to users, who may have a hard time determining which words in their profiles are important and which may be skewing their results to
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
collaboration by stressing, warning, and presenting keywords/summaries in multimedia. Effects of presenting keywords/summaries adaptively depending on situations are evaluated as to the decrease of not-/misunderstanding possibilities during the explanation on the Cyberspace. Moreover, the adaptive selection effects of keywords or
will be able to identify concepts and relationships from the dataset based on keyword searches in their own workspace and collaborate visually with other analysts using visualization tools such as a concept map view and a timeline view. The system allows analysts to parallelize the work by dividing initial sets of
With the development of internet, web information increases fast, how to filter information which users wanted quickly and accurately is becoming a big problem. But the traditional keyword based search system's recall rate and precision are yet to be improved. Kam-so, the user interesting collaborative filtering model
system. The retrieval system is able to retrieve videos based on emotional keyword query as well as arousal and valence query. The user's personal profile (gender, age, cultural background) was employed to improve the collaborative filtering in retrieval.
proposed a collective collaborative tagging (CCT) service architecture in which both service providers and individual users can merge folksonomy data (in the form of keyword tags) stored in different sources to build a larger, unified repository. We have also examined a range of algorithms that can be applied to different
The cloud computing paradigm has been receiving much attention recently, and data management applications such as e-business are potential candidates for deployment in the cloud. In this paper, we introduce CloudCDI, which is a platform for collaborative integration and management of community information in the cloud. We present the architecture for CloudCDI, which is based on cloud computing framework...
efficiency problems. Users preferences are almost ignored. So, the requirement of robust recommendation system is enhanced now a days. In this paper, we present review based service recommendation to dynamically recommend services to the users. Keywords are extracted from passive users reviews and a rating value is given to
Electronic Commerce has offered a convenient way for people to go shopping on the Internet. However, it is difficult for Internet customers to select a valuable item from the great number of various products available on line. When we use a keyword and search in a EC website, the ranking algorithm of products is
Web-based social networks, online personal profiles, keyword tagging, and online bookmarking are staples of Web 2.0-style applications. In this paper we report our investigation and implementation of these capabilities as a means for creating communities of like-minded faculty and researchers, particularly at minority
information retrieve modes as Fuzzy Constraint Keywords based Searching Mode, Location-based E-Map Browsing Mode, 3-D Tag-Cloud based Collaborative Sharing Mode, which can enable the user to choose suitable retrieve mode and find the useful entity information much quickly. Furthermore, this paper introduces the latest Web2.0
Social navigation and social tagging technologies enable user communities to assemble the collective wisdom, and use it to help community members in finding the right information. However, it takes a significantly-sized community to make a social system truly useful. The question addressed in this paper is whether collaborative information finding is feasible in the context of smaller communities...
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
the historical browsers' data for search keywords and provides users with most relevant web pages. All the users click-through activity such as number of times he visited, duration he spent, his mouse movements and several other variables are stored in database. The proposed system uses this database and process to rank
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
feedback and system log, then set up the social networks. According to the input keywords and types of recommender, more recommendation information can be generated. This model has been implemented as a recommendation module in an academic search system Gloss, deployed at the WSI Laboratory of Graduate University of Chinese
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.
Sharing and collaboration, keywords in current way of using the Web, are strongly present in the e-learning field, but usually not in educational content creation. There is a need that students participate even in lectures design, either by commenting, editing them or sharing notes with each other. This paper
Tagging is a process whereby users freely choose keywords to label Web objects in order to share or recover them later. Tags associated to an object by the user depict his viewpoint or perception. The perception of the target user can be enhanced by aggregating and analyzing the tags associated to an object by other
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