Having seen the implementation of the recommendation system based on the item-based and user-based similarity methodologies, we will explore some of the challenges:
Recommendation systems in general have the cold-start problem, that is, the algorithm would work fine when there is enough data, but with a lack of data, the accuracy would go for a toss
People's behavior might change with time or there could be behavior that is seasonal, and hence, these might have an impact on the recommendations
General methods to improve the accuracy are as follows:
While computing the similarity between the pairs (user or item), consider only the rows where at least one of the items of the pair has an entry. By this method, we will remove all the (0,0) pairs and hence could arrive at a more accurate similarity score.
Compute the similarities using multiple methods and identify the method that best suits the data.
Go for the hybrid approach where you combine multiple methods such as...
