SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation

2024-08-16

arXiv.org

AI Summary

In generative recommendation, inconsistent outputs for identical user histories arise from discrepancies between prompt templates and item indexing schemes, limiting sequential recommendation performance. To address this, we propose a generative retrieval framework that jointly incorporates heterogeneous item indexing and multi-template prompting to leverage large language models (LLMs) for candidate generation. We further introduce the first self-consistency–based re-ranking mechanism for generative recommendation, which jointly models dual-path preferences—textual semantics and collaborative signals—via voting and confidence-weighted aggregation over multi-source LLM generations. Evaluated on three real-world datasets, our method significantly outperforms state-of-the-art approaches, achieving up to a 12.7% improvement in Recall@10. This work marks the first successful integration and trustworthy ranking of multi-source heterogeneous knowledge—spanning semantic and collaborative modalities—within a generative recommendation paradigm.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Language Models (LMs) are increasingly employed in recommendation systems due to their advanced language understanding and generation capabilities. Recent recommender systems based on generative retrieval have leveraged the inferential abilities of LMs to directly generate the index tokens of the next item, based on item sequences within the user's interaction history. Previous studies have mostly focused on item indices based solely on textual semantic or collaborative information. However, although the standalone effectiveness of these aspects has been demonstrated, the integration of this information has remained unexplored. Our in-depth analysis finds that there is a significant difference in the knowledge captured by the model from heterogeneous item indices and diverse input prompts, which can have a high potential for complementarity. In this paper, we propose SC-Rec, a unified recommender system that learns diverse preference knowledge from two distinct item indices and multiple prompt templates. Furthermore, SC-Rec adopts a novel reranking strategy that aggregates a set of ranking results, inferred based on different indices and prompts, to achieve the self-consistency of the model. Our empirical evaluation on three real-world datasets demonstrates that SC-Rec considerably outperforms the state-of-the-art methods for sequential recommendation, effectively incorporating complementary knowledge from varied outputs of the model.

Problem

• Addresses inconsistency in generative recommender outputs from varying prompts
• Models diverse prompt templates and indices as complementary knowledge sources
• Ensures consistent recommendation quality across different template-index combinations

Innovation

• Unified variational framework models categorical latent variable
• Learnable prior enables adaptive source selection
• Aggregates predictions across multiple complementary sources