Predicting a user's next search query from recent interaction behaviors is a critical problem in modern e-commerce systems, particularly in scenarios where user intent evolves rapidly. Large Language Models (LLMs) offer strong semantic reasoning capabilities and have recently been adopted to enhance training data construction for next-query prediction. However, due to resource constraints on mobile devices, existing applications are deployed on cloud servers, resulting in high inference costs. In this paper, we propose RecGPT-Mobile, a framework that designs a lightweight LLM-based intent understanding agent to improve recommendation quality in mobile e-commerce scenarios. By deploying LLM directly on mobile devices, our approach can capture the evolving interests of users more quickly and adjust the recommendation results in real time. Extensive offline analyzes and online experiments demonstrate that our method significantly improves the accuracy of recommendation results, laying a practical path for LLM deployment in production-scale recommendation systems on mobile devices, as well as a scalable solution for integrating LLMs into real-world next-query prediction systems.

Promoting cold items to achieve rapid growth remains a fundamental challenge in billion-scale recommendation systems, as traditional natural/organic recommendation approaches primarily focus on Click-Through Rate (CTR) optimization, which naturally limits the exposure and spread of cold items. Recently, the AliBoost (V1) framework introduced boosting strategies to promote cold items to users most likely to click them. However, it still follows the same CTR-oriented optimization approach, thereby limiting long-term ecosystem health. In this work, we present the CTR-growth balanced boosting framework AliBoostV2, which explicitly considers the growth value of boosting candidate users and selects optimal users to balance immediate CTR goals with long-term growth potential. AliBoostV2 includes two key innovations: (1) a tailored Growth Potential Prediction module using counterfactual reasoning to estimate the additional natural traffic generated by each potential boosting exposure, and (2) a Dynamic CTR-Growth Boosting strategy that dynamically captures users' different interaction patterns across various time periods and delivers to users who can both click and contribute to growth simultaneously. AliBoostV2 has been deployed in production across Alibaba and Taobao's main platforms over the past six months, successfully cold-starting over one billion new items. Compared to the AliBoost (V1) framework, our approach achieves significant improvements of over 17.54% in both clicks and gross merchandise value (GMV) for cold items within a 180-day period. Extensive online analyses and rigorous A/B testing demonstrate the effectiveness of AliBoostV2 in addressing critical ecosystem challenges in billion-scale recommendation.

Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed products, users tend to browse a large number of items in succession, so the previously viewed items have a significant impact on users' behavior towards the following items. Therefore, traditional methods that mainly focus on improving the accuracy of recommended items are suboptimal for feed recommendations because they may recommend highly similar items. For feed recommendation, it is crucial to consider both the accuracy and diversity of the recommended item sequences in order to satisfy users' evolving interest when consecutively viewing items. To this end, this work proposes a general re-ranking framework named Multi-factor Sequential Re-ranking with Perception-Aware Diversification~(MPAD) to jointly optimize accuracy and diversity for feed recommendation in a sequential manner. Specifically, MPAD first extracts users' different scales of interests from their behavior sequences through graph clustering-based aggregations. Then, MPAD proposes two sub-models to respectively evaluate the accuracy and diversity of a given item by capturing users' evolving interest due to the ever-changing context and users' personal perception of diversity from an item sequence perspective. This is consistent with the browsing nature of the feed scenario. Finally, MPAD generates the return list by sequentially selecting optimal items from the candidate set to maximize the joint benefits of accuracy and diversity of the entire list. MPAD has been implemented in Taobao's homepage feed to serve the main traffic and provide services to recommend billions of items to hundreds of millions of users every day.

Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms. Though attractive, integrated recommendation requires the ranking methods to migrate from conventional user-item models to the new user-channel-item paradigm in order to better capture users' preferences on both item and channel levels. Moreover, practical feed recommendation systems usually impose exposure constraints on different channels to ensure user experience. This leads to greater difficulty in the joint ranking of heterogeneous items. In this paper, we investigate the integrated recommendation task with exposure constraints in practical recommender systems. Our contribution is forth-fold. First, we formulate this task as a binary online linear programming problem and propose a two-layer framework named Multi-channel Integrated Recommendation with Exposure Constraints~(MIREC) to obtain the optimal solution. Second, we propose an efficient online allocation algorithm to determine the optimal exposure assignment of different channels from a global view of all user requests over the entire time horizon. We prove that this algorithm reaches the optimal point under a regret bound of O (√T) with linear complexity. Third, we propose a series of collaborative models to determine the optimal layout of heterogeneous items at each user request. The joint modeling of user interests, cross-channel correlation, and page context in our models aligns more with the browsing nature of feed products than existing models. Finally, we conduct extensive experiments on both offline datasets and online A/B tests to verify the effectiveness of MIREC. The proposed framework has now been implemented on the homepage of Taobao to serve the main traffic.

Traditional Chinese pulse diagnosis, known as an empirical science, depends on the subjective experience. Inconsistent diagnostic results may be obtained among different practitioners. A scientific way of studying the pulse should be to analyze the objectified wrist pulse waveforms. In recent years, many pulse acquisition platforms have been developed with the advances in sensor and computer technology. And the pulse diagnosis using pattern recognition theories is also increasingly attracting attentions. Though many literatures on pulse feature extraction have been published, they just handle the pulse signals as simple 1-D time series and ignore the information within the class. This paper presents a generalized method of pulse feature extraction, extending the feature dimension from 1-D time series to 2-D matrix. The conventional wrist pulse features correspond to a particular case of the generalized models. The proposed method is validated through pattern classification on actual pulse records. Both quantitative and qualitative results relative to the 1-D pulse features are given through diabetes diagnosis. The experimental results show that the generalized 2-D matrix feature is effective in extracting both the periodic and nonperiodic information. And it is practical for wrist pulse analysis.

Pulse diagnosis, recognized as an important branch of traditional Chinese medicine (TCM), has a long history for health diagnosis. Certain features in the pulse are known to be related with the physiological status, which have been identified as biomarkers. In recent years, an electronic equipment is designed to obtain the valuable information inside pulse. Single-point pulse acquisition platform has the benefit of low cost and flexibility, but is time consuming in operation and not standardized in pulse location. The pulse system with a single-type sensor is easy to implement, but is limited in extracting sufficient pulse information. This paper proposes a novel system with optimal design that is special for pulse diagnosis. We combine a pressure sensor with a photoelectric sensor array to make a multichannel sensor fusion structure. Then, the optimal pulse signal processing methods and sensor fusion strategy are introduced for the feature extraction. Finally, the developed optimal pulse system and methods are tested on pulse database acquired from the healthy subjects and the patients known to be afflicted with diabetes. The experimental results indicate that the classification accuracy is increased significantly under the optimal design and also demonstrate that the developed pulse system with multichannel sensors fusion is more effective than the previous pulse acquisition platforms.

In this paper, we propose a new algorithm to solve the downlink resource allocation problem in LTE networks taking the channel conditions into account. We first formulate the problem as a convex optimization problem, which maximizes the aggregated utility of all users. Different from other papers of the same topic, we construct the utility function based on the Exponential Moving Average (EMA) rate instead of the instantaneous data rate. The advantage of this approach is that it guarantees the users with very bad channel conditions still to be scheduled, even if the number of Physical Resource Blocks (PRBs) is smaller than the number of users. The problem is solved optimally with the Lagrangian decomposition method. Extensive simulations have been carried out to compare our approach to the instantaneous data rate approach and to analyze the influence of the system parameters on the behavior of the algorithm.