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With the rapid growth of the internet and the spread of the information contained therein, the volume of information available on the web is more than the ability of users to manage, capture and keep the information up to date. One solution to this problem are personalization and recommender systems. Recommender systems use the comments of the group of users so that, to help people in that group more...
In this article the research of the effectiveness of similarity measures for recommender systems performed. The coefficients of similarity between vectors of user profiles and vector item profiles significantly affect the accuracy of prediction of the recommendations. This article shows that the cosine similarity measure and the Pearson correlation coefficient made error in predicting recommendations...
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 is natural to use the information to predict users' preference, such that an immediate...
It is often essential for people to consult with others and ask them about their past experience and thoughts when making choices. Exchanging ideas among people has become more meaningful since the extensive growth of information on the World Wide Web (WWW). People have access to tremendous amount of information, but choosing the most relevant information is of high effort. It was when recommender...
The tremendous growth in the amount of available web services (WS) impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, text based recommendations approaches depend mainly on user's perspective, languages and notations which easily decrease...
Trust recommender systems can depend on users' previous opinions and other trustworthy users' opinions on items suggest to them items they will like. However, it is very important to discern and find trustworthy users' opinions. In fact, it is easier for experts to find higher quality Web pages safely in search engines and trustworthy search histories are produced at the same time. Based on this observation,...
The Internet and the World Wide Web provide a way to store and share information, especially in academic fields. Community-based research paper sharing systems, such as CiteULike, have become popular among researchers. This paper proposes a framework for a tag-based research paper recommender system. The proposed approach exploits the use of sets of tags for recommending research papers to each user...
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