So far, we have discussed two main collaborative filtering approaches, namely user-based and item-based recommenders.
Even though they are capable of providing users with relevant recommendations, a major challenge that these approaches face is the sparsity of large datasets. Not all users will provide ratings on all the available items. Also, new items and new users tend to lack sufficient historical data to predict good recommendations. This is known as the cold start problem.
Further, the requirement for scalable recommendation algorithms remains the same along with the requirement to perform well in sparse datasets.
Also, some users tend to have a bias toward ratings, and the previous approaches have not made an attempt to correct this bias. Also, hidden patterns between the features of available items and the features of users that lead to certain ratings are not exploited.
Matrix factorization is another way of doing collaborative filtering, which...
