In this chapter, we discussed various methods of recommending products. We looked at different ways of recommending products to users, based on similarity in their purchase pattern, content, item to item comparison, and so on. As far as the accuracy is concerned, always the user-based collaborative filtering is giving better result in a real rating-based matrix as an input. Similarly, the choice of methods for a specific use case is really difficult, so it is recommended to apply all six different methods and the best one should be selected automatically and the recommendation should also get updated automatically.