What are the different issues of recommender system?
Lack of Data The more item and user data a recommender system has to work with, the stronger the chances of getting good recommendations. But it can be a chicken and egg problem – to get good recommendations, you need a lot of users, so you can get a lot of data for the recommendations.
What is sparsity problem in recommender system?
Data sparsity refers to the difficulty in finding sufficient reliable similar users since in general the active users only rated a small portion of items; • Cold start refers to the difficulty in generating accurate recommendations for the cold users who only rated a small number of items.
How does a recommender system work in real life?
Most recommender systems attempt to predict what the user would put in them if they rated the corresponding books. With too many “NaN”s, the recommender won’t have enough data to understand what the user likes. However, explicit rating is great if you can convince your users to give ratings to you.
Which is better, a higher or lower recommender system?
Higher is better. Commonly used metric for ranking evaluation of Top-N recommender systems, that only takes into account where the first relevant result occurs. We get more credit for recommending an item in which user rated on the top of the rank than on the bottom of the rank.
How to evaluate a top 10 recommendation system?
To evaluate top-10, we use hit rate, that is, if a user rated one of the top-10 we recommended, we consider it is a “hit”. The process of compute hit rate for a single user: Find all items in this user’s history in the training data. Intentionally remove one of these items ( Leave-One-Out cross-validation).
Can a recommender have too many NaNs?
With too many “NaN”s, the recommender won’t have enough data to understand what the user likes. However, explicit rating is great if you can convince your users to give ratings to you. Therefore, if you have the luxury of data and user ratings, then the evaluation metrics should be RMSE or MAE.