Extensive Review of state-of-the-art recommendation algorithms and analysis for novel applications i.e Analysis and Study of different algorithms that enable various recommendation systems to function. Further extend the algorithms for other applications.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning algorithms. It is able to generate more relevant recommendations tailored to the tastes of the user. One of the crucial components behind the working of a product recommendation engine is the recommender algorithm, which we would like to explore.
Apriori Algorithm, Matrix Factorisation Algorithm, TF-IDF
The study analyses the outcomes of several recommendation algorithms, as well as in-depth understanding of how these algorithms work and forecast user preferences or goods that are likely to be of interest to them. It also covers potential future work, such as the extension of each method to different applications.