Jianghao Lin

Generative recommendation has become a promising paradigm for producing complete recommendation lists, yet providing proper feedback to multiple candidates remains challenging. A key difficulty is the conflict between matching users' historical preferences and maintaining content diversity. Existing reward modeling and re-ranking methods usually rely on fixed objective aggregation or single-model judgment, which limits their flexibility and explainability in resolving such conflicts. To address this issue, we propose Debate4Rec, a multi-agent debate framework for feedback and candidate-list selection in generative recommendation. Debate4Rec acts as an external list-level feedback signal producer that provides interpretable feedback and selects the best candidate list. It decomposes list evaluation into two specialized agents: a Preference Agent for assessing alignment with user interests, and a Diversity Agent for evaluating category coverage, redundancy, and list richness. The agents first make independent judgments and then conduct a structured two-round debate to exchange evidence, revise decisions, and reach a final selection. Experiments on ML-1M and a Kuaishou short-video dataset show that Debate4Rec achieves competitive and often better performance than the LLM-based and traditional re-ranking baselines under the candidate-list selection setting. Ablation studies further confirm the effectiveness of role-specialized agents, structured debate, and the feedback-selector mechanism.

In an era dominated by information overload, effective recommender systems are essential for managing the deluge of data across digital platforms. Multi-stage cascade ranking systems are widely used in the industry, with retrieval and ranking being two typical stages. Retrieval methods sift through vast candidates to filter out irrelevant items, while ranking methods prioritize these candidates to present the most relevant items to users. Unlike studies focusing on the ranking stage, this survey explores the critical yet often overlooked retrieval stage of recommender systems. To achieve precise and efficient personalized retrieval, we summarize existing work in three key areas: improving similarity computation between user and item, enhancing indexing mechanisms for efficient retrieval, and optimizing training methods of retrieval. We also provide a comprehensive set of benchmarking experiments on three public datasets. Furthermore, we highlight current industrial applications through a case study on retrieval practices at a specific company, covering the entire retrieval process and online serving, along with practical implications and challenges. By detailing the retrieval stage, which is fundamental for effective recommendation, this survey aims to bridge the existing knowledge gap and serve as a cornerstone for researchers interested in optimizing this critical component of cascade recommender systems.

As large language models (LLMs) achieve remarkable success in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and are being actively explored currently. In this article, we focus on adapting and enhancing large language models for recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation realms, i.e., LLMs fail to effectively extract useful information from a pure textual context of long user behavior sequence, even if the length of context is well below the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely, R etrieval- e nhanced L arge La nguage models Plus (ReLLaX), which provides full-stack optimization from three perspectives, i.e., data, prompt, and parameter. For data-level enhancement, we design semantic user behavior retrieval (SUBR) to reduce the heterogeneity of the behavior sequence, thus lowering the difficulty for LLMs to extract the essential information from user behavior sequences. Although SUBR can improve the data quality, further increase in the sequence length will still raise its heterogeneity to a level where LLMs can no longer comprehend it. Hence, we further propose to perform prompt-level and parameter-level enhancement, with the integration of conventional recommendation models (CRMs). As for prompt-level enhancement, we apply soft prompt augmentation (SPA) to explicitly inject collaborative knowledge from CRMs into the prompt. The item representations of LLMs are thus more aligned with recommendation, helping LLMs better explore the item relationships in the sequence and facilitating comprehension. Finally, for parameter-level enhancement, we propose component fully-interactive LoRA (CFLoRA). By enabling sufficient interaction between the LoRA atom components, the expressive ability of LoRA is extended, making the parameters effectively capture more sequence information. Moreover, we present new perspectives to compare current LoRA-based LLM4Rec methods, i.e., from both a composite and a decomposed view. We theoretically demonstrate that the ways they employ LoRA for recommendation are degraded versions of our CFLoRA, with different constraints on atom component interactions. Extensive experiments are conducted on three real-world public datasets to demonstrate the superiority of ReLLaX compared with existing baseline models, as well as its capability to alleviate lifelong sequential behavior incomprehension. Our code is available. ¹