闻乐 发自 凹非寺 量子位 | 公众号 QbitAI 现在给鹅厂投简历，还来不来得及？ （老板别看 ） 这趟来蹲腾讯广告算法大赛的现场，我坐在台下直拍大腿—— 冠军直接揣走 200万奖金 ，亚军季军的奖励也都是六位数起步，哪怕只是站上答辩台，每个选手都能抱一台崭新的iPad走…… 于是我坐在观众席上反复确认一件事：我当年学计算机的时候，怎么没参加这种活动？？ 讲真，作为一个曾经也在实验室里敲代码的计算机学生，真正让我动了"求职"心思的，并不只是这些看得见的奖励。 更重要的，其实是通过大赛啃 实战级赛题 的机会、获得 实习或者直通offer 的通道，还有大厂提供的 算力和平台等资源 的诱惑。 用最前沿的问题练手 这场算法大赛的赛题是 「全模态生成式推荐」 ，名字听起来有点长，但它指向的，其实是当下广告和推荐系统里最前沿、也最接近真实业务 的一类问题。 简单说，以前做广告推荐，更多是盯着用户点过什么、看过什么，再配合一些文本特征来判断"你可能还想看点啥"。 但现在这套逻辑不太够用了，用户面对的早就不只是文字列表，而是大量图像、视频、音频混在一起的内容流。 全模态生成式推荐要做的，就是把这些来自不同模态的信息统一建 ...

编辑｜张倩 在国内，懂技术 —— 尤其是 AI 技术的年轻人，真的不缺崭露头角的机会。 前段时间，2025 年腾讯广告算法大赛结果揭晓，前 10 名队伍的全部成员都拿到了腾讯的录用意向书，冠军还拿到了 200 万元巨额奖金。 当时，看完选手们的答辩，腾讯公司副总裁蒋杰感慨地说，这届年轻人的知识储备令人惊叹，他们做出来的东西和工业界的实际工作非常接近，没有代差。 如果说大赛考的是一个已经被工业界解决的问题，选手们查查论文、复现方案，拼拼工程把问题解决掉倒也不是什么新鲜事。但看过今年赛题的人都知道，这次 摆在桌面上的，是一个仍在探索中的真实难题，没有现成答案，也不存在所谓「最优解」。 在业界，目前主要有两种方法在 PK。一种是已经用了很多年的 判别式方法 ，另一种是最近两三年兴起的 生成式方法 。 要理解两种方法的差异，我们可以举个例子：假设你是一个新来的班主任，想要根据小明同学的兴趣给他推荐合适的课外书。 也正因如此，比赛真正精彩的部分，其实不在排名本身，而在于： 这道题究竟难在哪里？工业界已经做了些什么？而这些年轻人，又给出了哪些实用的解法？ 在这篇文章中，我们将结合冠亚军团队的解决方案，来详细聊聊这些问题。 ...

Core Insights - The 2025 Tencent Algorithm Competition successfully held its finals in Shenzhen, with over 2800 teams participating globally, focusing on "multi-modal generative recommendation" [1][5] - The champion team "Echoch," consisting of members from Huazhong University of Science and Technology, Peking University, and University of Science and Technology of China, was awarded Tencent's offer and cash prizes [1] - The competition attracted over 8400 participants from nearly 30 countries, marking a historical high for overseas registrations [5] Competition Overview - The finals featured 20 teams that excelled in a rigorous selection process, showcasing innovative generative recommendation algorithms [1] - A special technical innovation award of 200,000 yuan was granted to the team "料峭春风吹酒醒" from the Institute of Computing Technology, Chinese Academy of Sciences [1] Technological Insights - The competition emphasized the application of advanced technologies such as LLM (Large Language Models) and MLLM (Multi-modal Large Language Models), leading to significant innovations in model performance [3] - The generative recommendation technology is seen as crucial for enhancing advertising precision and user experience, allowing for personalized ad recommendations [5] Industry Implications - Tencent's Vice President, Jiang Jie, highlighted the competition's role in attracting young talent to AI, reinforcing Tencent's commitment to technological innovation and collaboration between academia and industry [3] - The competition's dataset will be open-sourced post-event to foster further academic and industrial technological exchanges [5] Business Development - Tencent's Q3 financial report introduced the "Tencent Advertising AIM+" smart advertising product matrix, which optimizes marketing returns for advertisers [6] - The ongoing exploration of generative recommendation technologies within Tencent's advertising business aims to enhance user experience and drive commercial growth [6]

Group 1 - Tencent hosted an algorithm competition on August 1, attracting 8,400 participants from around the world [1] - The competition focuses on the cutting-edge topic of "multi-modal generative recommendation" [1]

Core Viewpoint - Tencent has launched the 2025 Tencent Advertising Algorithm Competition, focusing on "All-Modality Generative Recommendation," aiming to bridge academic and industry insights while providing a platform for technical talent to engage with Tencent's core business [3][10]. Group 1: Competition Highlights - The competition features a distinguished panel of judges, including top experts from academia and industry, ensuring that participants' proposals receive professional scrutiny and the opportunity for direct interaction with experts [5]. - A substantial prize pool of several million RMB is available, with the champion team eligible for over one million RMB in cash rewards, alongside internship offers for all finalists [9][7]. Group 2: Technical Focus - Participants will work with anonymized multimodal historical behavior data to predict user interactions with advertisements, encouraging exploration beyond traditional recommendation algorithms [8]. - The competition aims to attract talent capable of transforming academic theories into commercial value and challenging existing industry frameworks [10]. Group 3: Participation and Timeline - The competition is open to full-time students from global higher education institutions, including undergraduates, master's, doctoral, and postdoctoral candidates [13]. - Key dates include registration from June 16 to July 31, online preliminary rounds from August 1 to September 15, and finals in November, where participants will present their solutions [14].