This paper presents a cognition-based transmitter polarization optimization method for the conformal array space-time adaptive processing. First, we introduce the signal model and working mechanism of the airborne conformal array radar. Then the polarization characteristics of the target are recognized through the least squares criterion. Leveraging the estimated target scattering matrix, we further design the transmitter polarization optimization problem. Finally, the semidefinite relaxation approach is adopted to obtain the optimized transmit weight vector. Simulation results demonstrate the effectiveness of the proposed method in clutter cancellation.

Contemporary recommendation systems are designed to meet users' needs by delivering tailored lists of items that align with their specific demands or interests. In a multi-stage recommendation system, reranking plays a crucial role by modeling the intra-list correlations among items. The key challenge of reranking lies in the exploration of optimal sequences within the combinatorial space of permutations. Recent research proposes a generator-evaluator learning paradigm, where the generator generates multiple feasible sequences and the evaluator picks out the best sequence based on the estimated listwise score. The generator is of vital importance, and generative models are well-suited for the generator function. Current generative models employ an autoregressive strategy for sequence generation. However, deploying autoregressive models in real-time industrial systems is challenging. Firstly, the generator can only generate the target items one by one and hence suffers from slow inference. Secondly, the discrepancy between training and inference brings an error accumulation. Lastly, the left-to-right generation overlooks information from succeeding items, leading to suboptimal performance.

This paper proposes a reduced-dimension polarization-space-time adaptive processing (RD-PSTAP) algorithm for airborne conformal array (CFA) radar to achieve better clutter suppression performance at lower computational complexities. Firstly, we take polarization information into signal modeling. Subsequently, the dimension reduction matrix is derived based on the generalized sidelobe cancellation (GSC) structure, which incorporates the polarization information of the target and clutter into constructing the main channel and auxiliary channels, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm compared with the conventional RD algorithm.

An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task) and external behaviors (from other tasks). However, it is extremely challenging to conduct fast adaptations to real-time new trends while making full use of all historical behaviors in large-scale systems, due to the real-world limitations in real-time training efficiency and external behavior acquisition. To address these practical challenges, we propose a novel Long Short-Term Temporal Meta-learning framework (LSTTM) for online recommendation. It arranges user multi-source behaviors in a global long-term graph and an internal short-term graph, and conducts different GAT-based aggregators and training strategies to learn user short-term and long-term preferences separately. To timely capture users' real-time interests, we propose a temporal meta-learning method based on MAML under an asynchronous optimization strategy for fast adaptation, which regards recommendations at different time periods as different tasks. In experiments, LSTTM achieves significant improvements on both offline and online evaluations. It has been deployed on a widely-used online recommendation system named WeChat Top Stories, affecting millions of users.

Both explicit and implicit feedbacks can reflect user opinions on items, which are essential for learning user preferences in recommendation. However, most current recommendation algorithms merely focus on implicit positive feedbacks (e.g., click), ignoring other informative user behaviors. In this paper, we aim to jointly consider explicit/implicit and positive/negative feedbacks to learn user unbiased preferences for recommendation. Specifically, we propose a novel Deep feedback network (DFN) modeling click, unclick and dislike behaviors. DFN has an internal feedback interaction component that captures fine-grained interactions between individual behaviors, and an external feedback interaction component that uses precise but relatively rare feedbacks (click/dislike) to extract useful information from rich but noisy feedbacks (unclick). In experiments, we conduct both offline and online evaluations on a real-world recommendation system WeChat Top Stories used by millions of users. The significant improvements verify the effectiveness and robustness of DFN. The source code is in https://github.com/qqxiaochongqq/DFN.