DeepSeek launched an official account on Zhihu and published a technical article, "DeepSeek-V3/R1 Inference System Overview," detailing its model inference system optimization and cost-profit margin information for the first time, marking the end of the highly anticipated DeepSeek Open Source Week. The article highlights two key optimization goals for the DeepSeek-V3/R1 inference system: higher throughput and lower latency. To achieve these goals, DeepSeek employs large-scale cross-node expert parallelism (EP) technology, although...

Chinese e-commerce giant Pinduoduo has announced the formation of a large model team focused on e-commerce recommendations, further strengthening its commitment to AI in the sector. According to recent posts on X, the team is led by a former core member of Baidu's Phoenix Nest (Baidu's advertising system), whose salary is reportedly multiple times higher than at Baidu, demonstrating Pinduoduo's significant investment in talent acquisition. While Pinduoduo has not officially disclosed the leader's identity, an X user @TechBitDaily revealed in a February 23rd post that the team has already recruited numerous individuals from Baidu.

EasyRec is a recommendation system based on language models, developed by a team from the University of Hong Kong. Its uniqueness lies in analyzing emotional and detailed user behavior stories through a text behavior alignment framework to predict user preferences without requiring large amounts of user data. The system combines contrastive learning and collaborative language models, enabling accurate predictions of preferences for new users and new products, particularly excelling in zero-shot recommendation scenarios. EasyRec's plug-and-play features make it easy to integrate into existing recommendation systems, enhancing performance. The paper showcases EasyRec's performance across multiple...

Researchers from the University of Hong Kong and Tencent have proposed a new paradigm for multimodal recommendation systems called DiffMM, aiming to enhance the accuracy of short video recommendations. The system creates a graph containing user and video information and utilizes graph diffusion and contrastive learning techniques to better understand the relationship between users and videos, thereby achieving more accurate recommendations.The model method of DiffMM primarily includes three part