Previous studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to these documents even when their semantic quality is comparable to human-written ones. This phenomenon, known as source bias, threatens the sustainable development of the information access ecosystem. However, the underlying causes of source bias remain unexplored. In this paper, we explain the process of information retrieval with a causal graph and discover that PLM-based retrievers learn perplexity features for relevance estimation, causing source bias by ranking the documents with low perplexity higher. Theoretical analysis further reveals that the phenomenon stems from the positive correlation between the gradients of the loss functions in language modeling task and retrieval task. Based on the analysis, a causal-inspired inferencetime debiasing method is proposed, called Causal Diagnosis and Correction (CDC). CDC first diagnoses the bias effect of the perplexity and then separates the bias effect from the overall estimated relevance score. The rapid advancement of large language models (LLMs) has driven a significant increase in AIgenerated content (AIGC), leading to information retrieval (IR) systems that now index both humanwritten and LLM-generated contents (Cao et al., 2023; Dai et al., 2024b; 2025). However, recent studies (Dai et al., 2024a;c; Xu et al., 2024) have uncovered that Pretrained Language Model (PLM) based retrievers (Guo et al., 2022; Zhao et al., 2024) exhibit preferences for LLM-generated documents, ranking them higher even when their semantic quality is comparable to human-written content. This phenomenon, referred to as source bias, is prevalent among various popular PLM-based retrievers across different domains (Dai et al., 2024a). If the problem is not resolved promptly, human authors' creative willingness will be severely reduced, and the existing content ecosystem may collapse.

Large language models have become a powerful method for feature augmentation in recommendation systems. However, existing approaches relying on quick inference often suffer from incomplete feature coverage and insufficient specificity in feature descriptions, limiting their ability to capture fine-grained user preferences and undermining overall performance. Motivated by the recent success of inference scaling in math and coding tasks, we explore whether scaling inference can address these limitations and enhance feature quality. Our experiments show that scaling inference leads to significant improvements in recommendation performance, with a 12% increase in NDCG@10. The gains can be attributed to two key factors: feature quantity and specificity. In particular, models using extended Chain-of-Thought (CoT) reasoning generate a greater number of detailed and precise features, offering deeper insights into user preferences and overcoming the limitations of quick inference. We further investigate the factors influencing feature quantity, revealing that model choice and search strategy play critical roles in generating a richer and more diverse feature set. This is the first work to apply inference scaling to feature augmentation in recommendation systems, bridging advances in reasoning tasks to enhance personalized recommendation.