Language and imaginative and prescient fashions have skilled outstanding breakthroughs with the appearance of Transformer structure. Fashions like BERT and GPT have revolutionized pure language processing, whereas Imaginative and prescient Transformers have achieved important success in pc imaginative and prescient duties. This structure’s effectiveness has prolonged to advice techniques by fashions like SASRec and Bert4Rec. Nevertheless, regardless of these educational achievements, important challenges persist in implementing these options for large-scale industrial purposes, significantly in platforms like Kuaishou’s short-video advice system, the place real-time adaptation and complicated consumer conduct patterns demand extra refined approaches.
Suggestion techniques function by a two-stage course of: retrieval and rating. The retrieval part effectively selects potential gadgets from huge swimming pools utilizing light-weight dual-tower architectures, the place consumer and merchandise options are processed individually. The rating part then applies extra refined fashions to attain this filtered subset. This subject has developed from conventional collaborative filtering strategies to superior deep studying approaches. Sequential modeling has emerged as a vital element, with Transformer-based fashions like SASRec and BERT4Rec demonstrating outstanding enhancements in capturing consumer conduct patterns by their consideration mechanisms and bidirectional processing capabilities.
Researchers from Kuaishou Expertise, Beijing, China introduce KuaiFormer, an impressive transformation in large-scale content material advice techniques, departing from conventional rating estimation strategies to embrace a transformer-driven Subsequent Motion Prediction method. This modern framework, applied within the Kuaishou App’s short-video advice system, has demonstrated outstanding success in serving over 400 million day by day energetic customers. The system excels in real-time curiosity acquisition and multi-interest extraction, resulting in important enhancements in consumer engagement metrics. KuaiFormer’s profitable deployment supplies precious insights into implementing transformer fashions in industrial-scale advice techniques, providing sensible options for each technical and enterprise challenges.
The issue of short-video advice presents distinctive technical challenges in modeling consumer pursuits and predicting engagement. KuaiFormer processes consumer interplay information as sequences, the place every interplay contains each the video ID and numerous watching attributes comparable to viewing time, interplay labels, and class tags. The system makes use of these sequences to foretell customers’ subsequent possible engagements by a two-stage course of: coaching to seize real-time pursuits and inference to retrieve related content material. The structure employs refined embedding strategies for each discrete and steady attributes, using a Transformer-based spine impressed by the Llama structure to course of these advanced sequential patterns.
KuaiFormer operates inside a classy industrial streaming video advice infrastructure, serving as a vital element of Kuaishou’s retrieval system. The system processes consumer requests by a number of retrieval pathways, together with conventional approaches like Swing, GNN, Comirec, Dimerec, and GPRP, with KuaiFormer functioning as an extra pathway. The structure implements a multi-stage rating course of, progressing from pre-ranking by cascading ranks to remaining full rating. The system maintains steady enchancment by real-time processing of consumer suggestions alerts, together with watch time and social interactions, whereas optimizing effectivity by devoted embedding servers and GPU-accelerated retrieval algorithms like Faiss and ScaNN.
Complete efficiency evaluations reveal KuaiFormer’s superior effectiveness throughout a number of metrics. In offline testing, KuaiFormer considerably outperformed conventional approaches like SASRec and ComiRec, exhibiting a 25% enchancment in hit fee in comparison with GPRP. On-line A/B testing throughout Kuaishou’s main platforms revealed substantial enhancements in key metrics, together with video watch time will increase of 0.360%, 0.126%, and 0.411% throughout completely different eventualities. In depth hyperparameter evaluation revealed optimum configurations: sequence lengths past 64 confirmed diminishing returns, 6 question tokens supplied the very best stability of efficiency and effectivity, and 4-5 transformer layers achieved optimum accuracy. The modern merchandise compression technique proved significantly efficient, matching or exceeding the efficiency of uncompressed sequences whereas sustaining computational effectivity.
KuaiFormer represents a major development in industrial-scale advice techniques, significantly for short-video content material. The framework efficiently addresses key challenges by its modern mixture of multi-interest extraction, adaptive sequence compression, and sturdy coaching mechanisms. These technical achievements have translated into measurable enterprise impression, as evidenced by improved consumer engagement metrics and hit charges throughout Kuaishou’s platform. KuaiFormer’s success demonstrates that refined Transformer-based architectures will be successfully scaled for real-world purposes, dealing with billions of requests whereas sustaining excessive efficiency. This breakthrough paves the way in which for future developments in content material advice techniques and establishes a brand new benchmark for industrial-scale neural architectures.
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Asjad is an intern guide at Marktechpost. He’s persuing B.Tech in mechanical engineering on the Indian Institute of Expertise, Kharagpur. Asjad is a Machine studying and deep studying fanatic who’s at all times researching the purposes of machine studying in healthcare.
