Sequential Recommendation (SeqRec) aims to predict the next item by capturing sequential patterns from users' historical interactions, playing a crucial role in many real-world recommender systems. However, existing approaches predominantly adopt a direct forward computation paradigm, where the final hidden state of the sequence encoder serves as the user representation. We argue that this inference paradigm, due to its limited computational depth, struggles to model the complex evolving nature of user preferences and lacks a nuanced understanding of long-tail items, leading to suboptimal performance. To address this issue, we propose ReaRec, the first inference-time computing framework for recommender systems, which enhances user representations through implicit multi step reasoning. Specifically, ReaRecautoregressively feeds the sequence's last hidden state into the sequential recommender while incorporating special reasoning position embeddings to decouple the original item encoding space from the multi step reasoning space. Moreover, we introduce two lightweight reasoning-based learning methods, Ensemble Reasoning Learning (ERL) and Progressive Reasoning Learning (PRL), to further effectively exploit ReaRec's reasoning potential. Extensive experiments on five public real-world datasets and different SeqRec architectures demonstrate the generality and effectiveness of our proposed ReaRec. Remarkably, post-hoc analyses reveal that ReaRec significantly elevates the performance ceiling of multiple sequential recommendation backbones by approximately 30% 50%. Thus, we believe this work can open a new and promising avenue for future research in inference-time computing for sequential recommendation. The source code is available at https://github.com/TangJiakai/ReaRec.git.

Recommender systems (RSs) often suffer from the feedback loop phenomenon, i.e., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that reinforces homogeneous content and reduces user satisfaction. To this end, serendipity recommendations, which offer unexpected yet relevant items, are proposed. Recently, large language models (LLMs) have shown potential in serendipity prediction due to their extensive world knowledge and reasoning capabilities. However, they still face challenges in aligning serendipity judgments with human assessments, handling long user behavior sequences, and meeting the latency requirements of industrial RSs. To address these issues, we propose SERAL (Serendipity Recommendations with Aligned Large Language Models), a framework comprising three stages: (1) Cognition Profile Generation to compress user behavior into multi-level profiles; (2) SerenGPT Alignment to align serendipity judgments with human preferences using enriched training data; and (3) Nearline Adaptation to integrate SerenGPT into industrial RSs pipelines efficiently. Online experiments demonstrate that SERAL improves exposure ratio (PVR), clicks, and transactions of serendipitous items by 5.7%, 29.56%, and 27.6%, enhancing user experience without much impact on overall revenue. Now, it has been fully deployed in ''Guess What You Like'' on the Taobao App homepage.

Increasing demand for fashion recommendation raises a lot of challenges for online shopping platforms and fashion communities. In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process. In this paper, we demonstrate these two requirements can be satisfied via building a bridge between outfit generation and recommendation. Through large data analysis, we observe that people have similar tastes in individual items and outfits. Therefore, we propose a Personalized Outfit Generation (POG) model, which connects user preferences regarding individual items and outfits with Transformer architecture. Extensive offline and online experiments provide strong quantitative evidence that our method outperforms alternative methods regarding both compatibility and personalization metrics. Furthermore, we deploy POG on a platform named Dida in Alibaba to generate personalized outfits for the users of the online application iFashion.

Since TCP Incast has been identified as a catastrophic problem in many typical data center applications, a lot of efforts have been made to analyze or solve it. The analysis work intends to model Incast problem from certain perspective, and the solutions try to solve the problem through designing enhanced mechanisms or algorithms. However, the proposed models are either closely coupled with particular protocol version or dependent on empirical observations, and the solutions cannot eliminate Incast problem entirely because the underlying issues are not identified completely. There is little work which attempts to close the gap between “analyzing” and “solving”, and present a comprehensive understanding. In this paper, we provide an in-depth understanding of how TCP Incast problem happens. We build up an interpretive model which emphasizes particularly on describing qualitatively how various factors, including system parameters and mechanism variables, affect network performances in Incast traffic pattern, but not on calculating the accurate throughput. With this model, we give plausible explanations why the various solutions for TCP Incast problem can help, but do not solve it entirely.

Because of the terrible performance of TCP protocol in data center environment, DCTCP has been proposed as a TCP replacement, which uses a simple marking mechanism at switches and a few amendments at end hosts to adjust congestion window based on the extent of the congestion in networks. Thus, DCTCP can make a proper tradeoff between high throughput and low latency. However, through our observation, we discover that DCTCP causes severe oscillation of queue under some parameters and network configuration. Our perceptual analysis concludes that the rough single-threshold marking mechanism may be the essential reason. Therefore, we propose Double-Threshold DCTCP as an improvement of DCTCP. Then, by applying describing function method in nonlinear control theory, we analyze the stability of both DCTCP and Double-Threshold DCTCP, and theoretically explain why Double-Threshold DCTCP is more stable than DCTCP. At last, we validate theoretical analysis and conclude that the Double- Threshold DCTCP can achieve smaller queue, and the queue length of Double-Threshold DCTCP is less sensitive to the growing number of flows. Further, Double-Threshold DCTCP can postpone the throughput collapse caused by Incast traffic and reduce the tail latency in completion time experiment.