Improved Collaborative Filtering Recommendation Based on Classification and User Trust

When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.

Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion attacks.To address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different metrics.The rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other features.However,the methods suffer from mitigating intrusion attacks at a higher rate.This article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these deficiencies.The method involves analyzing service growth,network growth,and quality of service growth.The process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user requests.The proposed MFTEM model improves intrusion detection accuracy with higher performance.

Application of Machine Learning for Online Reputation Systems

Users on the Internet usually require venues to provide better purchasing recommendations.This can be provided by a reputation system that processes ratings to provide recommendations.The rating aggregation process is a main part of reputation systems to produce global opinions about the product quality.Naive methods that are frequently used do not consider consumer profiles in their calculations and cannot discover unfair ratings and trends emerging in new ratings.Other sophisticated rating aggregation methods that use a weighted average technique focus on one or a few aspects of consumers′profile data.This paper proposes a new reputation system using machine learning to predict reliability of consumers from their profile.In particular,we construct a new consumer profile dataset by extracting a set of factors that have a great impact on consumer reliability,which serve as an input to machine learning algorithms.The predicted weight is then integrated with a weighted average method to compute product reputation score.The proposed model has been evaluated over three Movie Lens benchmarking datasets,using 10-folds cross validation.Furthermore,the performance of the proposed model has been compared to previous published rating aggregation models.The obtained results were promising which suggest that the proposed approach could be a potential solution for reputation systems.The results of the comparison demonstrated the accuracy of our models.Finally,the proposed approach can be integrated with online recommendation systems to provide better purchasing recommendations and facilitate user experience on online shopping markets.