An Unified Search and Recommendation Foundation Model for Cold-Start Scenario

In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-domain models in the multi-task setting, with shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of features, labels, and sample distributions of individual tasks. With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks. We propose a novel framework called S\&R Multi-Domain Foundation, which uses LLM to extract domain invariant features, and Aspect Gating Fusion to merge the ID feature, domain invariant text features and task-specific heterogeneous sparse features to obtain the representations of query and item. Additionally, samples from multiple search and recommendation scenarios are trained jointly with Domain Adaptive Multi-Task module to obtain the multi-domain foundation model. We apply the S\&R Multi-Domain foundation model to cold start scenarios in the pretrain-finetune manner, which achieves better performance than other SOTA transfer learning methods. The S\&R Multi-Domain Foundation model has been successfully deployed in Alipay Mobile Application's online services, such as content query recommendation and service card recommendation, etc.Comment: CIKM 2023,6 page

Infer Implicit Contexts in Real-time Online-to-Offline Recommendation

Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from traditional recommendation where individual preference is mostly static, O2O recommendation should be dynamic to capture variation of users' purposes across time and location. However, precisely inferring users' real-time contexts information, especially those implicit ones, is extremely difficult, and it is a central challenge for O2O recommendation. In this paper, we propose a new approach, called Mixture Attentional Constrained Denoise AutoEncoder (MACDAE), to infer implicit contexts and consequently, to improve the quality of real-time O2O recommendation. In MACDAE, we first leverage the interaction among users, items, and explicit contexts to infer users' implicit contexts, then combine the learned implicit-context representation into an end-to-end model to make the recommendation. MACDAE works quite well in the real system. We conducted both offline and online evaluations of the proposed approach. Experiments on several real-world datasets (Yelp, Dianping, and Koubei) show our approach could achieve significant improvements over state-of-the-arts. Furthermore, online A/B test suggests a 2.9% increase for click-through rate and 5.6% improvement for conversion rate in real-world traffic. Our model has been deployed in the product of "Guess You Like" recommendation in Koubei.Comment: 9 pages,KDD,KDD201

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Device-Cloud Collaborative Recommendation via Meta Controller

On-device machine learning enables the lightweight deployment of recommendation models in local clients, which reduces the burden of the cloud-based recommenders and simultaneously incorporates more real-time user features. Nevertheless, the cloud-based recommendation in the industry is still very important considering its powerful model capacity and the efficient candidate generation from the billion-scale item pool. Previous attempts to integrate the merits of both paradigms mainly resort to a sequential mechanism, which builds the on-device recommender on top of the cloud-based recommendation. However, such a design is inflexible when user interests dramatically change: the on-device model is stuck by the limited item cache while the cloud-based recommendation based on the large item pool do not respond without the new re-fresh feedback. To overcome this issue, we propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender, and introduce a novel efficient sample construction from the causal perspective to solve the dataset absence issue of meta controller. On the basis of the counterfactual samples and the extended training, extensive experiments in the industrial recommendation scenarios show the promise of meta controller in the device-cloud collaboration.Comment: KDD 202

North Atlantic oscillation controls multidecadal changes in the North Tropical Atlantic−Pacific connection

By exciting subtropical teleconnections, sea surface temperature (SST) anomalies in the North Tropical Atlantic (NTA) during boreal spring can trigger El Niño-Southern Oscillation (ENSO) events in the following boreal winter, thereby providing a precursor for ENSO predictability. However, this NTA−ENSO connection is not stationary, and it varies considerably over multidecadal timescales, which cannot be directly explained by the Atlantic multidecadal oscillation or the global warming trend. Here we show that multidecadal changes in the NTA−ENSO connection are principally controlled by multidecadal variability associated with the North Atlantic Oscillation (NAO). During the positive phase of the NAO, the amplification of the NTA impact on ENSO mainly arises from strengthening of the boreal spring mean precipitation over the equatorial Atlantic and enhancement of the persistence of NTA SST anomalies, which enhance the NTA influence by exciting stronger and more persistent subtropical teleconnections. Our findings show that multidecadal variability of the NAO is key to understanding the impacts of the NTA SST on the tropical Pacific Ocean

North Atlantic oscillation controls multidecadal changes in the North Tropical Atlantic−Pacific connection

By exciting subtropical teleconnections, sea surface temperature (SST) anomalies in the North Tropical Atlantic (NTA) during boreal spring can trigger El Niño-Southern Oscillation (ENSO) events in the following boreal winter, thereby providing a precursor for ENSO predictability. However, this NTA-ENSO connection is not stationary, and it varies considerably over multidecadal timescales, which cannot be directly explained by the Atlantic multidecadal oscillation or the global warming trend. Here we show that multidecadal changes in the NTA-ENSO connection are principally controlled by multidecadal variability associated with the North Atlantic Oscillation (NAO). During the positive phase of the NAO, the amplification of the NTA impact on ENSO mainly arises from strengthening of the boreal spring mean precipitation over the equatorial Atlantic and enhancement of the persistence of NTA SST anomalies, which enhance the NTA influence by exciting stronger and more persistent subtropical teleconnections. Our findings show that multidecadal variability of the NAO is key to understanding the impacts of the NTA SST on the tropical Pacific Ocean