Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone.

History shows that, several cloned and fraudulent websites are developed in the World Wide Web to imitate legitimate websites, with the main motive of stealing sensitive important informational and economic resources from web surfers and financial organizations. This is a type of phishing attack, and it has cost the online networking community and all other stakeholders thousands of million Dollars. Hence, efficient counter measures are required to detect phishing URLs accurately. Machine learning algorithms are very popular for all types of data analysis and these algorithms are depicting good results in battling with phishing when we compare with other classic anti-phishing approaches, like cyber security awareness workshops, visualization approaches giving some legal countermeasures to these cyber-attacks. In this research work authors investigated different Machine Learning techniques applicability to identify phishing attacks and distinguishes their pros and cons. Specifically, various types of Machine Learning techniques are applied to reveal diverse approaches which can be used to handle anti-phishing approaches. In this work authors have experimentally compared large number of ML techniques on different phishing datasets by using various metrics. The main focus in this comparison is to showcase advantages and disadvantages of ML predictive models and their actual performance in identifying phishing attacks.

Matrix Factorization is a long established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources, we present a novel parallel computation framework utilizing community information available in the rating network. Our proposed approach named as Community-based Matrix Factorization(CBMF), parallelizes matrix factorization technique by dividing the network into communities using existing community detection algorithms. We prove that this parallel approach not only increases the quality of recommendations in connection with Root Mean Square Error (RMSE), but also yields substantial performance improvement. We empirically evaluate our idea on diverse datasets and present comprehensive experimental results. These results serve as empirical evidence of the effectiveness and performance gains offered by our parallel computation framework.