TagLLM: A Fine-Grained Tag Generation Approach for Note Recommendation

Large Language Models (LLMs) have shown promising potential in E-commerce community recommendation. While LLMs and Multimodal LLMs (MLLMs) are widely used to encode notes into implicit embeddings, leveraging their generative capabilities to represent notes with interpretable tags remains unexplored. In the field of tag generation, traditional close-ended methods heavily rely on the design of tag pools, while existing open-ended methods applied directly to note recommendations face two limitations: (1) MLLMs lack guidance during generation, resulting in redundant tags that fail to capture user interests; (2) The generated tags are often coarse and lack fine-grained representation of notes, interfering with downstream recommendations. To address these limitations, we propose TagLLM, a fine-grained tag generation method for note recommendation. TagLLM captures user interests across note categories through a User Interest Handbook and constructs fine-grained tag data using multimodal CoT Extraction. A Tag Knowledge Distillation method is developed to equip small models with competitive generation capabilities, enhancing inference efficiency. In online A/B test, TagLLM increases average view duration per user by 0.31%, average interactions per user by 0.96%, and page view click-through rate in cold-start scenario by 32.37%, demonstrating its effectiveness.

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system

Modeling multi-interests has arisen as a core problem in real-world RS. Current multi-interest retrieval methods pose three major challenges: 1) Interests, typically extracted from predefined external knowledge, are invariant. Failed to dynamically evolve with users' real-time consumption preferences. 2) Online inference typically employs an over-exploited strategy, mainly matching users' existing interests, lacking proactive exploration and discovery of novel and long-tail interests. To address these challenges, we propose a novel retrieval framework named SPARC(Soft Probabilistic Adaptive Retrieval Model via Codebooks). Our contribution is two folds. First, the framework utilizes Residual Quantized Variational Autoencoder (RQ-VAE) to construct a discretized interest space. It achieves joint training of the RQ-VAE with the industrial large scale recommendation model, mining behavior-aware interests that can perceive user feedback and evolve dynamically. Secondly, a probabilistic interest module that predicts the probability distribution over the entire dynamic and discrete interest space. This facilitates an efficient "soft-search" strategy during online inference, revolutionizing the retrieval paradigm from "passive matching" to "proactive exploration" and thereby effectively promoting interest discovery. Online A/B tests on an industrial platform with tens of millions daily active users, have achieved substantial gains in business metrics: +0.9% increase in user view duration, +0.4% increase in user page views (PV), and a +22.7% improvement in PV500(new content reaching 500 PVs in 24 hours). Offline evaluations are conducted on open-source Amazon Product datasets. Metrics, such as Recall@K and Normalized Discounted Cumulative Gain@K(NDCG@K), also showed consistent improvement. Both online and offline experiments validate the efficacy and practical value of the proposed method.

Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models

The feedback loop in industrial recommendation systems reinforces homogeneous content, creates filter bubble effects, and diminishes user satisfaction. Recently, large language models(LLMs) have demonstrated potential in serendipity recommendation, thanks to their extensive world knowledge and superior reasoning capabilities. However, these models still face challenges in ensuring the rationality of the reasoning process, the usefulness of the reasoning results, and meeting the latency requirements of industrial recommendation systems (RSs). To address these challenges, we propose a method that leverages llm to dynamically construct user knowledge graphs, thereby enhancing the serendipity of recommendation systems. This method comprises a two stage framework:(1) two-hop interest reasoning, where user static profiles and historical behaviors are utilized to dynamically construct user knowledge graphs via llm. Two-hop reasoning, which can enhance the quality and accuracy of LLM reasoning results, is then performed on the constructed graphs to identify users' potential interests; and(2) Near-line adaptation, a cost-effective approach to deploying the aforementioned models in industrial recommendation systems. We propose a u2i (user-to-item) retrieval model that also incorporates i2i (item-to-item) retrieval capabilities, the retrieved items not only exhibit strong relevance to users' newly emerged interests but also retain the high conversion rate of traditional u2i retrieval. Our online experiments on the Dewu app, which has tens of millions of users, indicate that the method increased the exposure novelty rate by 4.62%, the click novelty rate by 4.85%, the average view duration per person by 0.15%, unique visitor click through rate by 0.07%, and unique visitor interaction penetration by 0.30%, enhancing user experience.