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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
Most prior works in automatic examination rating focus on rating objective questions because natural language processing techniques cannot provide sufficient support for subjective answers rating. This paper presents a novel solution to subjective answer rating which is based on semantic matching of keywords rather
In most cases, users are unable to precisely translate their information needs into a query format for the search system to process. Users often submit queries containing terms or keywords that do not match with their intended information. That is why user normally reformulates queries several times to gain more
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
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.