Stochastically Robust Personalized Ranking for LSH Recommendation Retrieval

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global performance, which produces a ranking score for each individual item. However, it may be sub-optimal because the scoring function applies to each item individually and does not explicitly consider the mutual in uence between items, as well as the di erences of users' preferences or intents. erefore, we propose a personalized re-ranking model for recommender systems. e proposed re-ranking model can be easily deployed as a follow-up modular a er any ranking algorithm, by directly using the existing ranking feature vectors. It directly optimizes the whole recommendation list by employing a transformer structure to e ciently encode the information of all items in the list. Speci cally, the Transformer applies a self-a ention mechanism that directly models the global relationships between any pair of items in the whole list. We conrm that the performance can be further improved by introducing pre-trained embedding to learn personalized encoding functions for di erent users. Experimental results on both o ine benchmarks and real-world online e-commerce systems demonstrate the signi cant improvements of the proposed re-ranking model.