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:推荐系统通常从物品语料库中检索物品进行个性化推荐。然而,这种基于检索的推荐范式面临两个限制:1)语料库中的人工生成物品可能无法满足用户多样化的信息需求,2)用户通常通过被动和低效的反馈(如点击)调整推荐。现今,基于AI生成的内容(AI-Generated Content,AIGC)在各个领域都取得了显著的成功,提供了克服这些限制的潜力:1)生成式AI可以产生个性化物品以满足用户特定的信息需求,2)新出现的ChatGPT通过自然语言指令显著促进用户更准确地表达信息需求。在这种情况下,AIGC的繁荣指引了下一代推荐范式的方向,包括两个新的目标:1)通过生成式AI生成个性化内容,2)集成用户指令以指导内容生成。为此,我们提出了一种新的生成式推荐范式GeneRec,采用AI生成器个性化生成内容,并利用用户指令获取用户的信息需求。具体而言,我们通过一个导师的预处理将用户的指令和传统反馈(如点击)输出为生成指导。在获得指导后,我们通过AI编辑器和AI创建器来重新利用现有物品和创建新物品。最终,GeneRec能够执行内容的检索、重新利用和创造,以满足用户的信息需求。此外,为确保生成的物品的可靠性,我们强调各种忠实度检查,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创建器的可行性,并展示了有希望的结果。