Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naïvely training all tasks together in one model often degrades performance, and exhaustively searching through combinations of task groupings can be prohibitively expensive.

De-Chuan Zhan is the corresponding author. Figure 1: Conditional entropy and transformed distance. In Figure. 1, we use The relationship is worth further research.Figure 2: Conditional entropy and transformed distance with different n and m In this section, we describe the implementation details of GDFM and all the compared methods. 2 3.1 Dataset processing Criteo There are 8 numerical features and 9 categorical features in the Criteo dataset. Each bin is represented with a 32-dimensional embedding. We found that increasing the number of bins or embedding size could not improve performance significantly.

Predicting conversion rate (e.g., the probability that a user will purchase an item) is

The "dueling bandits" problem, initially proposed as a model for similar recommendation systems A number of works in recent years explore online problems where an agent responds to the decision-maker's actions, influencing their reward. The "revealed preferences" literature involves a similar requirement of learning a mapping Some recent work has begun to explore the problem of designing optimal strategies in a repeated game against agents who adapt their strategies over time using a no-regret algorithm. As such, the empirical probability of b must be close to 1 /2. We make use of a lemma from [2], which we restate here. Lemma 8. Consider two vectors We prove local learnability results for each case.

The Recommender then observes the Agent's selected item

However, their performance usually suffers from the overfitting problem seriously.

Importantly, an item cannot be recommended twice to the same user. The probabilities that a user likes each item are unknown.