Item Recommendation using Bicluster Approach

Abstract

By using the complex, highly dimensional data analysis technique known as biclustering, rows and columns are simultaneously grouped. In the context of recommendation systems, biclustering may be used to identify subgroups of users and items that have strong linkages and are thus likely to make suitable recommendations for one another. This method has a number of advantages over traditional recommendation systems, including the ability to handle noisy and sparse data and the possibility to uncover previously unknown patterns of user-item interactions. The essential concepts behind biclustering-based recommendation systems are outlined in this abstract, along with some recent advancements. We also discuss some of this strategy's disadvantages and benefits and provide suggestions for more research. We believe that biclustering-based systems for recommendation might significantly improve the accuracy and significance of item suggestions in a range of diverse applications.