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of services to various clients by not considering different clients requirements and therefore not able to fulfill clients actualize needs. For this method, we introduce a Positive Negative Keyword-Aware Service Recommendation strategy, abbreviated PNKASR, to minimize the raised difficulties and for accuracy point of
meet users’ personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically
on Map-Reduce, named PASR, is proposed in this paper. It aims at presenting a personalized ranking list and recommending the most appropriate services to the users from big data environment. In this method, keywords are used to indicate users' preferences, and a user based Collaborative Filtering algorithm is adopted to
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
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
single machine. Our motivating application is recommenders, which typically deal with big numbers of users and items, but other applications might benefit as well, like keyword search. In this paper, we propose a parallel top-k MapReduce algorithm that, unlike existing MapReduce solutions, manages to handle cases in which
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