PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information

With the increase of content pages and interactive buttons in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challenges in multi-domain and multi-task recommendations. The core of multi-task and multi-domain recommendation is to accurately capture user interests in multiple scenarios given multiple user behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work (\textbf{PEPNet})} for multi-domain and multi-task recommendation. PEPNet takes personalized prior information as input and dynamically scales the bottom-level Embedding and top-level DNN hidden units through gate mechanisms. \textit{Embedding Personalized Network (EPNet)} performs personalized selection on Embedding to fuse features with different importance for different users in multiple domains. \textit{Parameter Personalized Network (PPNet)} executes personalized modification on DNN parameters to balance targets with different sparsity for different users in multiple tasks. We have made a series of special engineering optimizations combining the Kuaishou training framework and the online deployment environment. By infusing personalized selection of Embedding and personalized modification of DNN parameters, PEPNet tailored to the interests of each individual obtains significant performance gains, with online improvements exceeding 1\% in multiple task metrics across multiple domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million users every day.Comment: Accepted by KDD 202

Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation

Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditional sequential recommendation always mainly considers the collected positive feedback such as click, purchase, etc. However, in short-video platforms such as TikTok, video viewing behavior may not always represent positive feedback. Specifically, the videos are played automatically, and users passively receive the recommended videos. In this new scenario, users passively express negative feedback by skipping over videos they do not like, which provides valuable information about their preferences. Different from the negative feedback studied in traditional recommender systems, this passive-negative feedback can reflect users' interests and serve as an important supervision signal in extracting users' preferences. Therefore, it is essential to carefully design and utilize it in this novel recommendation scenario. In this work, we first conduct analyses based on a large-scale real-world short-video behavior dataset and illustrate the significance of leveraging passive feedback. We then propose a novel method that deploys the sub-interest encoder, which incorporates positive feedback and passive-negative feedback as supervision signals to learn the user's current active sub-interest. Moreover, we introduce an adaptive fusion layer to integrate various sub-interests effectively. To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback. The experiments on two large-scale datasets verify that the proposed method can significantly outperform state-of-the-art approaches. The code is released at https://github.com/tsinghua-fib-lab/RecSys2023-SINE.Comment: Accepted by RecSys'2

TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou

Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction systems. Conventional algorithms mostly follow two cascading stages: a simple General Search Unit (GSU) for fast and coarse search over tens of thousands of long-term behaviors and an Exact Search Unit (ESU) for effective Target Attention (TA) over the small number of finalists from GSU. Although efficient, existing algorithms mostly suffer from a crucial limitation: the \textit{inconsistent} target-behavior relevance metrics between GSU and ESU. As a result, their GSU usually misses highly relevant behaviors but retrieves ones considered irrelevant by ESU. In such case, the TA in ESU, no matter how attention is allocated, mostly deviates from the real user interests and thus degrades the overall CTR prediction accuracy. To address such inconsistency, we propose \textbf{TWo-stage Interest Network (TWIN)}, where our Consistency-Preserved GSU (CP-GSU) adopts the identical target-behavior relevance metric as the TA in ESU, making the two stages twins. Specifically, to break TA's computational bottleneck and extend it from ESU to GSU, or namely from behavior length $10^2$ to length $10^4-10^5$, we build a novel attention mechanism by behavior feature splitting. For the video inherent features of a behavior, we calculate their linear projection by efficient pre-computing \& caching strategies. And for the user-item cross features, we compress each into a one-dimentional bias term in the attention score calculation to save the computational cost. The consistency between two stages, together with the effective TA-based relevance metric in CP-GSU, contributes to significant performance gain in CTR prediction.Comment: Accepted by KDD 202

Mixed Attention Network for Cross-domain Sequential Recommendation

In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, especially for new users. One promising line of work is the cross-domain recommendation, which trains models with data across multiple domains to improve the performance in data-scarce domains. Recent proposed cross-domain sequential recommendation models such as PiNet and DASL have a common drawback relying heavily on overlapped users in different domains, which limits their usage in practical recommender systems. In this paper, we propose a Mixed Attention Network (MAN) with local and global attention modules to extract the domain-specific and cross-domain information. Firstly, we propose a local/global encoding layer to capture the domain-specific/cross-domain sequential pattern. Then we propose a mixed attention layer with item similarity attention, sequence-fusion attention, and group-prototype attention to capture the local/global item similarity, fuse the local/global item sequence, and extract the user groups across different domains, respectively. Finally, we propose a local/global prediction layer to further evolve and combine the domain-specific and cross-domain interests. Experimental results on two real-world datasets (each with two domains) demonstrate the superiority of our proposed model. Further study also illustrates that our proposed method and components are model-agnostic and effective, respectively. The code and data are available at https://github.com/Guanyu-Lin/MAN.Comment: WSDM 202

Intervention-induced enhancement in intrinsic brain activity in healthy older adults

This study examined the effects of a multimodal intervention on spontaneous brain activity in healthy older adults. Seventeen older adults received a six-week intervention that consisted of cognitive training, Tai Chi exercise, and group counseling, while 17 older adults in a control group attended health knowledge lectures. The intervention group demonstrated enhanced memory and social support compared to the control group. The amplitude of low frequency fluctuations (ALFF) in the middle frontal gyrus, superior frontal gyrus, and anterior cerebellum lobe was enhanced for the intervention group, while the control group showed reduced ALFF in these three regions. Moreover, changes in trail-making performance and well-being could be predicted by the intervention-induced changes in ALFF. Additionally, individual differences in the baseline ALFF were correlated with intervention-related changes in behavioral performance. These findings suggest that a multimodal intervention is effective in improving cognitive functions and well-being and can induce functional changes in the aging brain. The study extended previous training studies by suggesting resting-state ALFF as a marker of intervention-induced plasticity in older adults

Alleviation of DSS-induced colitis in mice by a new-isolated Lactobacillus acidophilus C4

IntroductionProbiotic is adjuvant therapy for traditional drug treatment of ulcerative colitis (UC). In the present study, Lactobacillus acidophilus C4 with high acid and bile salt resistance has been isolated and screened, and the beneficial effect of L. acidophilus C4 on Dextran Sulfate Sodium (DSS)-induced colitis in mice has been evaluated. Our data showed that oral administration of L. acidophilus C4 remarkably alleviated colitis symptoms in mice and minimized colon tissue damage.MethodsTo elucidate the underlying mechanism, we have investigated the levels of inflammatory cytokines and intestinal tight junction (TJ) related proteins (occludin and ZO-1) in colon tissue, as well as the intestinal microbiota and short-chain fatty acids (SCFAs) in feces.ResultsCompared to the DSS group, the inflammatory cytokines IL-1β, IL-6, and TNF-α in L. acidophilus C4 group were reduced, while the antioxidant enzymes superoxide dismutase (SOD), glutathione (GSH), and catalase (CAT) were found to be elevated. In addition, proteins linked to TJ were elevated after L. acidophilus C4 intervention. Further study revealed that L. acidophilus C4 reversed the decrease in intestinal microbiota diversity caused by colitis and promoted the levels of SCFAs.DiscussionThis study demonstrate that L. acidophilus C4 effectively alleviated DSS-induced colitis in mice by repairing the mucosal barrier and maintaining the intestinal microecological balance. L. acidophilus C4 could be of great potential for colitis therapy

Osmotic Stress Induced Cell Death in Wheat Is Alleviated by Tauroursodeoxycholic Acid and Involves Endoplasmic Reticulum Stress–Related Gene Expression

Although, tauroursodeoxycholic acid (TUDCA) has been widely studied in mammalian cells because of its role in inhibiting apoptosis, its effects on plants remain almost unknown, especially in the case of crops such as wheat. In this study, we conducted a series of experiments to explore the effects and mechanisms of action of TUDCA on wheat growth and cell death induced by osmotic stress. Our results show that TUDCA: (1) ameliorates the impact of osmotic stress on wheat height, fresh weight, and water content; (2) alleviates the decrease in chlorophyll content as well as membrane damage caused by osmotic stress; (3) decreases the accumulation of reactive oxygen species (ROS) by increasing the activity of antioxidant enzymes under osmotic stress; and (4) to some extent alleviates osmotic stress–induced cell death probably by regulating endoplasmic reticulum (ER) stress–related gene expression, for example expression of the basic leucine zipper genes bZIP60B and bZIP60D, the binding proteins BiP1 and BiP2, the protein disulfide isomerase PDIL8-1, and the glucose-regulated protein GRP94. We also propose a model that illustrates how TUDCA alleviates osmotic stress–related wheat cell death, which provides an important theoretical basis for improving plant stress adaptation and elucidates the mechanisms of ER stress–related plant osmotic stress resistance