This article explains how the inference component of OpenAI’s o1 model can be implemented using Monte‑Carlo Tree Search, detailing the action space, rollout process, UCT scoring, and best‑path selection, with a concrete walkthrough of Microsoft’s open‑source rStar code.
This article explains the fundamentals of vector databases, how high‑dimensional vector data is generated and stored, reviews common ANN search algorithms such as Flat, k‑means and LSH, discusses benchmarking and product selection, and demonstrates practical integration of vector stores with LLMs using LangChain and Python code.
The article presents a comprehensive study of POI (point‑of‑interest) recognition and alias linking within travel search, detailing background challenges, a multi‑stage algorithmic framework, extensive offline experiments, and the resulting improvements in online conversion and relevance.
This presentation details Alibaba Entertainment's video search system, covering its business scope, user‑value metrics, a layered algorithm framework, relevance challenges, multi‑modal retrieval, deep semantic relevance techniques, model selection, asymmetric twin‑tower deployment, multi‑stage knowledge distillation, and practical effect cases.
The live session presented Alibaba Entertainment’s senior algorithm expert discussing Youku’s video search business, relevance and ranking models, multimodal search challenges, and practical AI techniques, offering attendees a comprehensive view of modern video retrieval systems and their implementation.
This article reviews the evolution of Taobao’s search and recommendation technology, tracing its journey from simple statistical models and rule‑based systems through large‑scale machine learning and real‑time online learning to modern deep‑learning and cognitive intelligence approaches that drive e‑commerce innovation.
This comprehensive guide explains linear search, binary search, bubble sort, insertion sort, selection sort, quicksort, and merge sort, detailing their concepts, step‑by‑step procedures, and providing complete, ready‑to‑run C code examples for each algorithm.
This article explains Monte Carlo Tree Search (MCTS), covering its origin in AlphaGo, fundamental algorithm steps, node‑selection strategies such as UCB, strengths and weaknesses, enhancements, historical background, and recent research developments in artificial intelligence.
This article presents concise explanations, step‑by‑step procedures, and visual illustrations for ten core algorithms—including QuickSort, HeapSort, MergeSort, Binary Search, BFPRT, DFS, BFS, Dijkstra, Dynamic Programming, and Naive Bayes—highlighting their principles, complexities, and typical use cases.
This article explores the mathematical foundations of Google’s PageRank algorithm, detailing how Larry Page and Sergey Brin modeled web page ranking as a Markov process, addressed challenges like dangling pages, and introduced stochastic and primitivity adjustments to achieve reliable search results.
