Trust aware social context filtering using Shuffled frog leaping algorithm

In the past few years social context filtering (SCF) systems have become trendier to solve the problem of information overload. Conventional SCF approaches utilize preferences of all nearest neighbors to recommend the items. However, in practice preferences of credible peers / true friends with similar interests influence the decision making process. Thus need of trust aware approaches is being increasingly felt. Incorporating user's web of trust information though solves the sparsity and cold start problem prevailing in conventional social context filtering techniques but issue of scalability still remains. The work presents Shuffled frog leaping algorithm (SFLA) based SCF approach to develop trust aware system which is capable of handling all the issues addressed above. The approach performs social context modeling using SFLA based clustering. Subsequently, only the trusted neighbors participate in the process of computing most relevant items. Experimental evaluation over Movielens dataset establishes that SFLA based SCF model significantly outperforms conventional K-means approach. Evaluation over Epinions (rating and trust) dataset further substantiates the accuracy of SFLA based trust aware approach over mean absolute error metric.