Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation

TL;DR

This paper introduces D3, a novel decoding method for LLM-based recommendation systems that reduces amplification bias and improves diversity by adjusting normalization and encouraging less frequent tokens.

Contribution

The paper proposes Debiasing-Diversifying Decoding (D3), a new decoding approach that addresses amplification bias and homogeneity in LLM-based recommendations, improving accuracy and diversity.

Findings

D3 effectively reduces amplification bias in LLM decoding.

D3 enhances diversity and reduces repetitive recommendations.

Experimental results show improved recommendation accuracy.

Abstract

Adapting Large Language Models (LLMs) for recommendation requires careful consideration of the decoding process, given the inherent differences between generating items and natural language. Existing approaches often directly apply LLMs' original decoding methods. However, we find these methods encounter significant challenges: 1) amplification bias -- where standard length normalization inflates scores for items containing tokens with generation probabilities close to 1 (termed ghost tokens), and 2) homogeneity issue -- generating multiple similar or repetitive items for a user. To tackle these challenges, we introduce a new decoding approach named Debiasing-Diversifying Decoding (D3). D3 disables length normalization for ghost tokens to alleviate amplification bias, and it incorporates a text-free assistant model to encourage tokens less frequently generated by LLMs for counteracting recommendation homogeneity. Extensive experiments on real-world datasets demonstrate the method's effectiveness in enhancing accuracy and diversity. The code is available at https://github.com/SAI990323/DecodingMatters.