  Recently, researchers have uncovered that neural retrieval models prefer
AI-generated content (AIGC), called source bias. Compared to active search
behavior, recommendation represents another important means of information
acquisition, where users are more prone to source bias. Furthermore, delving
into the recommendation scenario, as AIGC becomes integrated within the
feedback loop involving users, data, and the recommender system, it
progressively contaminates the candidate items, the user interaction history,
and ultimately, the data used to train the recommendation models. How and to
what extent the source bias affects the neural recommendation models within
feedback loop remains unknown. In this study, we extend the investigation of
source bias into the realm of recommender systems, specifically examining its
impact across different phases of the feedback loop. We conceptualize the
progression of AIGC integration into the recommendation content ecosystem in
three distinct phases-HGC dominate, HGC-AIGC coexist, and AIGC dominance-each
representing past, present, and future states, respectively. Through extensive
experiments across three datasets from diverse domains, we demonstrate the
prevalence of source bias and reveal a potential digital echo chamber with
source bias amplification throughout the feedback loop. This trend risks
creating a recommender ecosystem with limited information source, such as AIGC,
being disproportionately recommended. To counteract this bias and prevent its
escalation in the feedback loop, we introduce a black-box debiasing method that
maintains model impartiality towards both HGC and AIGC. Our experimental
results validate the effectiveness of the proposed debiasing method, confirming
its potential to disrupt the feedback loop.
