Time, Politics and Homelessness in Contemporary Japan

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The Social Virtue Of Blind Deference

Recently, it has become popular to account for knowledge and other epistemic states in terms of epistemic virtues. The present paper focuses on an epistemic virtue relevant when deferring to others in testimonial contexts. It is argued that, while many virtue epistemologists will accept that epistemic virtue can be exhibited in cases involving epistemically motivated hearers, carefully vetting their testimonial sources for signs of untrustworthiness prior to deferring, anyone who accepts that also has to accept that an agent may exhibit epistemic virtue in certain cases of blind deference, involving someone soaking up everything he or she is told without any hesitation. Moreover, in order to account for the kind of virtue involved in the relevant cases of blind deference, virtue epistemologists need to abandon a widespread commitment to personalism, i.e., the idea that virtue is possessed primarily on account of features internal to the psychology of the person, and accept that some virtues are social virtues, possessed in whole or in large part on account of the person being embedded in a reliable social environment

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table