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Internet sources using a keyword-based place model as input. Based on external relevance criteria the system finds and pre-selects only those sources that are more relevant, and an adaptive scheduling algorithm continuously select content that are relevant, timely, in accordance with the place model, sensitive to immediate
As personalization technologies are widely used, preference extraction is becoming important. In this work, we propose a preference extraction method on the basis of applications that are installed on a user's smart device. In this method, keywords are extracted from descriptions of the installed applications on an
articles. Then, we design a three-layered graph-based recommendation model that integrates fine-grained co-authorship as well as author–paper, paper–citation, and paper-keyword relations. Our model effectively generates query-oriented recommendations using a simple random walk algorithm. Extensive experiments
This paper describes algorithmic decision support that facilitates recommendation of course schedules personal- ized to the background and interests of a given student. More specifically, recommendations are made with pri- oritized consideration of four categories of information: (1) degree requirements, (2) student interests, (3) student performance, and (4) time-to-degree. All four categories of...
selection behaviors. In particular, considering that such influential surrounding context information in microblogs includes keywords related to restaurant assessment, we propose a method for automatically determining the keywords to extract the context information by analyzing online reviews, which have been gathered also
public display raises specific challenges that may limit the applicability of existing recommender systems. In this paper, we explore the creation of a recommender system for public situated displays that is able to autonomously select relevant content from Internet sources using keywords as input. This type of recommender
of content. The main contribution of FIRSt is an integrated strategy that enables a content-based recommender to infer user interests by applying machine learning techniques, both on official item descriptions provided by a publisher and on freely keywords which users adopt to annotate relevant items. Static content and
The scale of the social web has integrated users in order to organize shared resources. Users freely associate keywords (tags) to resources. This collection of tags creates a folksonomy. Folksonomy is a collaborative tagging system, which has grown popular with its simplicity of free tagging. However, it rises up a
emerged as one successful approach to tackle the problem of information overload. Traditional recommender systems suggest research items using well-known text mining techniques, however they fail when there are no identical keywords to match searches. In order to overcome this and other limitations, several studies have been
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