关键词: 推荐系统, 多任务学习, 双重鲁棒, 逆倾向得分, 不确定性估计

Abstract:

Recommender systems play an important role in internet applications，with the core task of recommending the most relevant items to users. However，the large?scale，sparse，or high?dimensional datasets often lead to serious bias problems. In recent years，multi?task learning has become an effective method for addressing bias in recommender systems，allowing multiple related tasks to be learned simultaneously，thus fully utilizing the intrinsic structure and correlation of the datasets. Researchers recently proposed entire space counterfactual conversion rate prediction，which uses inverse propensity score and doubly robust methods to estimate the performance of recommendation algorithms. However，theoretical analysis has revealed that inaccurate propensity score estimation and interpolation errors often lead to estimation bias，which frequently occurs in practice，thereby affecting the accuracy and reliability of recommendations. We therefore introduce uncertainty estimation，combining multi?task learning to measure the reliability of feedback data by computing the probability distribution for each user，to mitigate model overfitting in sparse or noisy data and effectively improve system generalization to reduce bias. Experimental results show that multi?task learning with uncertainty estimation can better adapt to uncertain environments and has broad prospects in recommender systems.

Key words: recommendation system, multi?task learning, doubly robust, inverse propensity score, uncertainty estimation