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Recently, content-based Spam detection frameworks are receiving a significant amount of attention by academic researchers and industrial practitioners. However, the anticipated wide scale proliferation is limited (mainly) because of two important shortcomings: (1) high false alarm rate that results in moving legitimate messages into Spam folders; and (2) inability to self-learn a user's profile; as a result, they are unable to identify useful Spam (we call it Good Spam) that might be of great interest to a user's personal or business aspirations. In this paper, we propose USpam, a system that uses ontologies to model features that are extracted from a user's profile. The features are given to machine learning classifiers -J48 and Naive Bayes -that learn a user centric model of Good Spam or Bad Spam. As a result, the system puts a message into a user's inbox if its contents are relevant to his interests. The USpam is evaluated on ENRON Spam datasets; and the results of experiments reveal that false alarms are reduced by 10% to 30% compared with existing prior art without compromising the detection accuracy.

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Abstract-Till date, the dominant part of Recommender Systems (RS) work focusing on single domain, i.e. for films, books and shopping and so on. However, human inclinations may traverse over numerous areas. Thus, utilization practices on related things from various domains can be valuable for RS to make recommendations. Academic articles, such as research papers are the way to express ideas and thoughts for the research community. However, there have been a lot of journals available which recognize these technical writings. In addition, journal selection procedure should consider user experience about the journals in order to recommend users most relevant journal. In this work of journal recommendation system, the data about the user experience targeting various aspects of journals has been gathered which addresses user experience about any journal. In addition, data set of archive articles has been developed considering the user experience in this regard. Moreover, the user experience and gathered data of archives are analyzed using two different frameworks based on semantics in order to have better consolidated recommendations. Before submission, we offer services on behalf of the research community that exploit user reviews and relevant data to suggest suitable journal according to the needs of the author.