Practical counterfactual policy learning for top-k recommendations
For building recommender systems, a critical task is to learn a policy with collected feedback
(eg, ratings, clicks) to decide which items to be recommended to users. However, it has been …
(eg, ratings, clicks) to decide which items to be recommended to users. However, it has been …
Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation
The weighted squared loss is a common component in several Collaborative Filtering (CF)
algorithms for item recommendation, including the representative implicit Alternating Least …
algorithms for item recommendation, including the representative implicit Alternating Least …