A Framework for an Efficient Recommendation System Using Time and Fairness Constraint Based Web Usage Mining Technique

Abstract

Users prefer to use various websites like Facebook, Gmail, and YouTube. We can make the system predict what pages we expect in the future and give the users what they have requested. Based on the data gathered and analyzed, we can predict the user's future navigation patterns in response to the user's requests. In order to track down users’ navigational sessions, the web access logs created at a specific website are processed. Grouping the user session data is then done into clusters, where inter-cluster similarities are minimized, although the intra-cluster similarities are maximised. Recent clustering and fairness analysis research has focused on centric-based methods such as k-median and k-means clustering. We propose improved constrained based clustering (ICBC) based on fair algorithms for managing Hierarchical Agglomerative Clustering (HAC) that apply fairness constraints regardless of distance linking parameters, simplifying clustering fairness trials for HAC and intended for various protected groups compared to vanilla HAC techniques. Also, this ICBC is used to select an algorithm whose inherent bias matches a specific problem, and then to adjust the optimization criterion of any distinct algorithm to take the constraints on interpretation to improve the efficiency of clustering. We show that our proposed algorithm finds fairer clustering by evaluation on the NASA dataset by balancing the constraints of the problem.