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Many misunderstandings can occur during remote interaction due to different user domain competency levels, different cognitive capacity of users as well as different user backgrounds. In this paper, we propose an adaptive keyword/summary presentation approach that aims at identifying potential misunderstandings of
An important and challenging task in any keyword-based search system in text documents or relational databases is the capability of the system to find additional results besides the actual search results and present them to the users as recommendations. This function allows the records that might be of interest to the
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
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
keyword. This graph is built using a carefully selected one-parameter set of keywords. By varying this parameter - the level of meaningfulness - we transition the document-representing graph from a trivial path graph into a large random graph. During such a conversion, as the parameter is varied over its range, the graph
data. Besides, most of existing recommendation systems present the same static ratings and rankings of items to different users without considering their different needs, and therefore fails to meet users personalized requirements. This paper uses English language Keyword list and domain thesaurus based personalized
to external hierarchical resource to polish accuracy of text matching. Also, a whole framework of text processing, keyword extraction and information matching is applied firstly among Chinese SMEs complementarity identification. By using machine learning algorithm, complementarities are digitalized and potential
In traditional collaborative filtering recommendation, the matrix sparsity and cold start restricted the accuracy of system. In this paper, we develop a way to enhance the recommendation effectiveness by merging neighborhood relationship and users keyword of social network information into collaborative filtering. We
in this study. The first is a baseline approach which is based on simple keyword mapping technique. The second approach, Co-Citation Selection (CCS), is based on the collaborative filtering in which neighboring papers is selected and weighted into publication citation prediction. To compare between two approaches, we
hidden relationships between items based upon users' interactions with them, and we can also perform ontology mining to learn which keywords are semantically-related to other keywords based upon how they are used together by similar users as recorded in search engine query logs. The biggest challenge to this collaborative
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
' phrase in their title or keywords or abstracts and retrieve totally 4,579 publications. Spearman correlation rank test is used to examine the hypotheses. Analysis of collected data shows that publication's impact is significantly and positively associated with collaboration indicators based on authors' affiliations. However
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.
Tagging with free form tags is becoming an increasingly important indexing mechanism. However, free form tags have characteristics that require special treatment when used for searching or recommendation because they show much more variation than controlled keywords. In this paper we present a method that puts this
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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