Proposes proactive fairness in recommenders by guiding user preferences toward long-tail items, avoiding preference misalignment from direct insertion.
A proactive guiding strategy for item-side fairness in interactive recommendation systems aims to align user preferences with long-tail items by iteratively steering user interests, rather than directly inserting underrepresented items into recommendations, which can cause preference misalignment. This approach leverages the concept of preference guidance to gradually shift user embeddings toward target items—often those from less popular or underexposed providers—by recommending intermediate items that balance interaction probability and guiding efficacy .
Item-side fairness, also referred to as provider fairness, emphasizes equitable exposure for items and their creators, particularly benefiting niche or emerging providers who are often overshadowed by popular items in traditional recommendation systems . The imbalance in exposure is commonly attributed to popularity bias, where widely consumed items dominate recommendations, leading to a feedback loop that further marginalizes long-tail content .
The Iterative Preference Guidance (IPG) framework exemplifies such a proactive strategy by computing an IPG score that combines the likelihood of user interaction with the potential of an item to guide preferences toward a target . This model-agnostic, post-processing method integrates with existing sequential recommenders and dynamically adjusts rankings based on real-time user feedback, enabling a more natural and effective transition toward underrepresented items .
By focusing on long-term preference evolution rather than immediate exposure adjustments, proactive strategies like IPG maintain recommendation accuracy while improving fairness metrics such as catalog coverage and provider diversity . This is particularly relevant in AI research, where ensuring equitable outcomes without compromising user satisfaction remains a key challenge in deploying fair and sustainable recommender systems .
This research addresses the critical challenge of item-side fairness within interactive recommendation systems, where algorithmic feedback loops often exacerbate popularity bias and marginalize long-tail items. Traditional mitigation approaches frequently rely on "direct insertion," where the system explicitly injects unpopular items into the recommendation list to ensure exposure. However, the authors identify a significant flaw in this method: it results in preference misalignment, forcing users to engage with content that deviates sharply from their established tastes, which can degrade user satisfaction and trust. Instead of forcing exposure, this paper proposes a "proactive guiding strategy," which reframes the fairness objective as a long-term process of shaping user interests rather than a short-term manipulation of recommendation lists.
The key contribution of this work is a framework that dynamically guides user preferences toward long-tail items through a sequence of strategic interactions. By modeling the recommendation process as a trajectory, the strategy aims to gradually expand a user's interest profile to encompass fairer items without causing abrupt friction. The technical insight lies in recognizing that user preferences are malleable; by strategically selecting intermediate items that bridge the gap between a user's current preferences and the target long-tail items, the system can align user satisfaction with fairness goals. This approach effectively balances the trade-off between optimizing for immediate engagement (accuracy) and achieving long-term equity (fairness).
This work matters because it offers a more sustainable and human-centric solution to the fairness-accuracy dilemma in recommendation systems. By shifting the paradigm from static exposure correction to dynamic preference guidance, it enables platforms to promote diversity and mitigate the "rich-get-richer" effect without sacrificing user retention. This has significant implications for the health of digital ecosystems, such as e-commerce and content streaming, where equitable exposure is essential for new creators and niche products, but cannot come at the cost of alienating users with irrelevant or intrusive recommendations.
This paper introduces a proactive guiding strategy to enhance item-side fairness in interactive recommendation systems. Unlike traditional approaches that rely on post-hoc adjustments or direct insertion of underrepresented (long-tail) items, the proposed method subtly guides user preferences before recommendations are made. By dynamically adjusting the interactive process—such as modifying item presentations or user feedback signals—the system encourages users toward diverse, long-tail items without artificial insertion, thus mitigating preference misalignment and improving fairness. The approach leverages reinforcement learning to balance fairness and utility, ensuring that the guiding strategy adapts to user behavior while promoting equitable exposure.
The key contributions include: 1. A fairness-aware guiding mechanism that operates at the interaction level, avoiding the pitfalls of direct item insertion (e.g., reduced user satisfaction or model performance degradation). 2. A theoretical framework connecting fairness to long-tail item exposure, demonstrating how proactive guidance can improve diversity without sacrificing relevance. 3. Empirical validation showing that the method outperforms baseline fairness interventions in terms of both fairness metrics (e.g., Gini coefficient) and recommendation quality (e.g., NDCG).
This work is significant because it addresses a critical limitation in recommender systems: the tendency to reinforce popularity biases through user feedback loops. By focusing on preference shaping rather than post-hoc corrections, the strategy offers a more sustainable path to fairness, particularly in dynamic, user-driven environments. It also bridges gaps between fairness research and practical deployment, where intrusive fairness interventions often fail. For researchers and practitioners, this paper provides a blueprint for integrating fairness into the core interaction dynamics of recommendation systems, rather than treating it as an afterthought.