Refractory or relapsed B lymphoblastic leukemia (B-ALL) patients have a dismal outcome with current therapy. We treated 42 primary refractory/hematological relapsed (R/R) and 9 refractory minimal residual disease by flow cytometry (FCM-MRD) B-ALL patients with optimized second generation CD19-directed CAR-T cells. The CAR-T-cell infusion dosages were initially ranged from 0.05 to 14 × 10/kg and were eventually settled at 1 × 10/kg for the most recent 20 cases. 36/40 (90%) evaluated R/R patients achieved complete remission (CR) or CR with incomplete count recovery (CRi), and 9/9 (100%) FCM-MRD patients achieved MRD. All of the most recent 20 patients achieved CR/CRi. Most cases only experienced mild to moderate CRS. 8/51 cases had seizures that were relieved by early intervention. Twenty three of twenty seven CR/CRi patients bridged to allogeneic hematopoietic stem cell transplantation (allo-HCT) remained in MRD with a median follow-up time of 206 (45-427) days, whereas 9 of 18 CR/CRi patients without allo-HCT relapsed. Our results indicate that a low CAR-T-cell dosage of 1 × 10/kg, is effective and safe for treating refractory or relapsed B-ALL, and subsequent allo-HCT could further reduce the relapse rate.

Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is -users are recommended a page of items and provide feedback; and then the system recommends a new page of items. To effectively capture such interaction for recommendations, we need to solve two key problems -(1) how to update recommending strategy according to user's real-time feedback, and 2) how to generate a page of items with proper display, which pose tremendous challenges to traditional recommender systems. In this paper, we study the problem of page-wise recommendations aiming to address aforementioned two challenges simultaneously. In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its own recommendation strategy. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent reinforcement learning based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in existing model-free RL model [10]-(i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based reinforcement learning techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.

As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been situated in a static or one-shot setting, where the protected groups of items are fixed, and the model provides a one-time fairness solution based on fairnessconstrained optimization. This fails to consider the dynamic nature of the recommender systems, where attributes such as item popularity may change over time due to the recommendation policy and user engagement. For example, products that were once popular may become no longer popular, and vice versa. As a result, the system that aims to maintain long-term fairness on the item exposure in different popularity groups must accommodate this change in a timely fashion.Novel to this work, we explore the problem of long-term fairness in recommendation and accomplish the problem through dynamic fairness learning. We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process. We tackle this problem by proposing a fairness-constrained reinforcement learning algorithm for recommendation, which models the recommendation problem as a Constrained Markov Decision Process (CMDP), so that the model can dynamically adjust its recommendation policy to make sure the fairness requirement is always satisfied when the environment changes. Experiments on several real-world datasets verify our framework's superiority in terms of recommendation performance, short-term fairness, and long-term fairness. CCS CONCEPTS• Information systems → Recommender systems; • Computing methodologies → Sequential decision making.