  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 inefficient passive feedback, e.g.,
clicks. Nowadays, AI-Generated Content (AIGC) has revealed significant success,
offering the potential to overcome these limitations: 1) generative AI can
produce personalized items to satisfy users' information needs, and 2) the
newly emerged large language models significantly reduce the efforts of users
to precisely express information needs 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. Specifically, we pre-process users' instructions
and traditional feedback 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. Eventually,
GeneRec can perform content retrieval, repurposing, and creation to satisfy
users' information needs. Besides, to ensure the trustworthiness of the
generated items, we emphasize various fidelity checks. Moreover, we provide a
roadmap to envision future developments of GeneRec and several domain-specific
applications of GeneRec with potential research tasks. Lastly, we study the
feasibility of implementing AI editor and AI creator on micro-video generation.
