Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator

• URL: http://arxiv.org/abs/2409.09253v1
• Date: Sat, 14 Sep 2024 01:45:04 GMT
• Title: Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator
• Authors: Jun Yin, Zhengxin Zeng, Mingzheng Li, Hao Yan, Chaozhuo Li, Weihao Han, Jianjin Zhang, Ruochen Liu, Allen Sun, Denvy Deng, Feng Sun, Qi Zhang, Shirui Pan, Senzhang Wang,
• Abstract summary: We propose Twin-Tower Dynamic Semantic Recommender (T TDS), the first generative RS which adopts dynamic semantic index paradigm. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender. The proposed T TDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.
• Score: 60.07198935747619
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Owing to the unprecedented capability in semantic understanding and logical reasoning, the pre-trained large language models (LLMs) have shown fantastic potential in developing the next-generation recommender systems (RSs). However, the static index paradigm adopted by current methods greatly restricts the utilization of LLMs capacity for recommendation, leading to not only the insufficient alignment between semantic and collaborative knowledge, but also the neglect of high-order user-item interaction patterns. In this paper, we propose Twin-Tower Dynamic Semantic Recommender (TTDS), the first generative RS which adopts dynamic semantic index paradigm, targeting at resolving the above problems simultaneously. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender, hierarchically allocating meaningful semantic index for items and users, and accordingly predicting the semantic index of target item. Furthermore, a dual-modality variational auto-encoder is proposed to facilitate multi-grained alignment between semantic and collaborative knowledge. Eventually, a series of novel tuning tasks specially customized for capturing high-order user-item interaction patterns are proposed to take advantages of user historical behavior. Extensive experiments across three public datasets demonstrate the superiority of the proposed methodology in developing LLM-based generative RSs. The proposed TTDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.

Related papers

• Towards Scalable Semantic Representation for Recommendation [65.06144407288127]
  Mixture-of-Codes is proposed to construct semantic IDs based on large language models (LLMs) Our method achieves superior discriminability and dimension robustness scalability, leading to the best scale-up performance in recommendations.
  arXiv  Detail & Related papers  (2024-10-12T15:10:56Z)
• Large Language Model Empowered Embedding Generator for Sequential Recommendation [57.49045064294086]
  Large Language Model (LLM) has the potential to understand the semantic connections between items, regardless of their popularity. We present LLMEmb, an innovative technique that harnesses LLM to create item embeddings that bolster the performance of Sequential Recommender Systems.
  arXiv  Detail & Related papers  (2024-09-30T03:59:06Z)
• Semantic Codebook Learning for Dynamic Recommendation Models [55.98259490159084]
  Dynamic sequential recommendation (DSR) can generate model parameters based on user behavior to improve personalization of sequential recommendation. It faces the challenges of large parameter search space and sparse and noisy user-item interactions, which reduces the applicability of the generated model parameters. The Semantic Codebook Learning for Dynamic Recommendation Models (SOLID) framework presents a significant advancement in DSR by effectively tackling these challenges.
  arXiv  Detail & Related papers  (2024-07-31T19:25:25Z)
• CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation [18.986613405565514]
  Large Language Models (LLMs) are pretrained on vast corpora of text for sequential recommendation. We propose a two-stage LLM finetuning framework that finetunes a pretrained LLM in a two-tower fashion using a mixture of two contrastive losses and a language modeling loss. Our model significantly outperforms many state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-05-03T18:51:19Z)
• Enhancing Few-shot NER with Prompt Ordering based Data Augmentation [59.69108119752584]
  We propose a Prompt Ordering based Data Augmentation (PODA) method to improve the training of unified autoregressive generation frameworks. Experimental results on three public NER datasets and further analyses demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-05-19T16:25:43Z)
• Contrastive Proposal Extension with LSTM Network for Weakly Supervised Object Detection [52.86681130880647]
  Weakly supervised object detection (WSOD) has attracted more and more attention since it only uses image-level labels and can save huge annotation costs. We propose a new method by comparing the initial proposals and the extension ones to optimize those initial proposals. Experiments on PASCAL VOC 2007, VOC 2012 and MS-COCO datasets show that our method has achieved the state-of-the-art results.
  arXiv  Detail & Related papers  (2021-10-14T16:31:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.