Recommender systems typically retrieve items from an item corpus for personalized recommendations. However, such a retrieval-based recommender paradigm faces two limitations: 1) the human-generated items in the corpus might fail to satisfy the users' diverse information needs, and 2) users usually adjust the recommendations via passive and inefficient feedback such as clicks. Nowadays, AI-Generated Content (AIGC) has revealed significant success across various domains, offering the potential to overcome these limitations: 1) generative AI can produce personalized items to meet users' specific information needs, and 2) the newly emerged ChatGPT significantly facilitates users to express information needs more precisely via natural language instructions. In this light, the boom of AIGC points the way towards the next-generation recommender paradigm with two new objectives: 1) generating personalized content through generative AI, and 2) integrating user instructions to guide content generation. To this end, we propose a novel Generative Recommender paradigm named GeneRec, which adopts an AI generator to personalize content generation and leverages user instructions to acquire users' information needs. Specifically, we pre-process users' instructions and traditional feedback (e.g., clicks) via an instructor to output the generation guidance. Given the guidance, we instantiate the AI generator through an AI editor and an AI creator to repurpose existing items and create new items, respectively. Eventually, GeneRec can perform content retrieval, repurposing, and creation to meet users' information needs. Besides, to ensure the trustworthiness of the generated items, we emphasize various fidelity checks such as authenticity and legality checks. Lastly, we study the feasibility of implementing the AI editor and AI creator on micro-video generation, showing promising results.

翻译：推荐系统通常从项目库中检索项目以进行个性化推荐。然而，这种基于检索的推荐范式面临两个局限：1）库中人类生成的项目可能无法满足用户多样化的信息需求；2）用户通常通过点击等被动且低效的反馈来调整推荐。如今，人工智能生成内容（AIGC）已在多个领域展现出显著成功，为克服这些局限提供了可能：1）生成式AI可生成个性化项目以满足用户特定的信息需求；2）新出现的ChatGPT显著便利了用户通过自然语言指令更精确地表达信息需求。基于此，AIGC的繁荣为下一代推荐范式指明了方向，其具有两个新目标：1）通过生成式AI生成个性化内容；2）整合用户指令以指导内容生成。为此，我们提出了一种名为GeneRec的新型生成式推荐范式，它采用AI生成器实现个性化内容生成，并利用用户指令获取用户信息需求。具体而言，我们通过一个指令处理器将用户指令和传统反馈（如点击）预处理为生成指导。基于该指导，我们通过AI编辑器和AI创建者实例化AI生成器，分别用于改造现有项目和创建新项目。最终，GeneRec可执行内容检索、改造和创建以满足用户信息需求。此外，为确保生成项目的可信性，我们强调了各种真实性检查，如真实性和合法性检查。最后，我们研究了在微视频生成中实现AI编辑器和AI创建者的可行性，展示了令人鼓舞的结果。