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 influence between items, as well as the differences of users’ preferences or intents. Therefore, we propose a personalized re-ranking model for recommender systems. The proposed re-ranking model can be easily deployed as a follow-up modular after any ranking algorithm, by directly using the existing ranking feature vectors. It directly optimizes the whole recommendation list by employing a transformer structure to efficiently encode the information of all items in the list. Specifically, the Transformer applies a self-attention mechanism that directly models the global relationships between any pair of items in the whole list. We confirm that the performance can be further improved by introducing pre-trained embedding to learn personalized encoding functions for different users. Experimental results on both offline benchmarks and real-world online e-commerce systems demonstrate the significant improvements of the proposed re-ranking model.

Recommendation with multiple objectives is an important but difficult problem, where the coherent difficulty lies in the possible conflicts between objectives. In this case, multi-objective optimization is expected to be Pareto efficient, where no single objective can be further improved without hurting the others. However existing approaches to Pareto efficient multi-objective recommendation still lack good theoretical guarantees. In this paper, we propose a general framework for generating Pareto efficient recommendations. Assuming that there are formal differentiable formulations for the objectives, we coordinate these objectives with a weighted aggregation. Then we propose a condition ensuring Pareto efficiency theoretically and a two-step Pareto efficient optimization algorithm. Meanwhile the algorithm can be easily adapted for Pareto Frontier generation and fair recommendation selection. We specifically apply the proposed framework on E-Commerce recommendation to optimize GMV and CTR simultaneously. Extensive online and offline experiments are conducted on the real-world E-Commerce recommender system and the results validate the Pareto efficiency of the framework. To the best of our knowledge, this work is among the first to provide a Pareto efficient framework for multi-objective recommendation with theoretical guarantees. Moreover, the framework can be applied to any other objectives with differentiable formulations and any model with gradients, which shows its strong scalability.

Recommender systems offer critical services in the age of mass information. Personalized ranking have been attractive both for content providers and customers due to its ability of creating a user-specific ranking on the item set. Although the powerful factor-analysis methods including latent factor model and deep neural network models have achieved promising results, they still suffer from the challenging issues, such as sparsity of recommendation data, uncertainty of optimization, and etc. To enhance the accuracy and generalization of recommender system, in this paper, we propose a deep generative ranking (DGR) model under the Wasserstein auto-encoder framework. Specifically, DGR simultaneously generates the pointwise implicit feedback data (via a Beta-Bernoulli distribution) and creates the pairwise ranking list by sufficient exploiting both interacted and non-interacted items for each user. DGR can be efficiently inferred by minimizing its penalized evidence lower bound. Meanwhile, we theoretically analyze the generalization error bounds of DGR model to guarantee its performance in extremely sparse feedback data. A series of experiments on four large-scale datasets (Movielens (20M), Netflix, Epinions and Yelp in movie, product and business domains) have been conducted. By comparing with the state-of-the-art methods, the experimental results demonstrate that DGR consistently benefit the recommendation system in ranking estimation task, especially for the near-cold-start-users (with less than five interacted items).