Beyond Predictions: Building Recommendation Systems That Truly Know Your Users

Author: Denis Avetisyan

This review explores how incorporating detailed user understanding – through techniques like Know Your Customer (KYC) analysis – is driving a new generation of recommendation systems capable of enhanced accuracy, diversity, and serendipity.

Performance comparisons reveal a clear progression in effectiveness, starting with a baseline approach, then improving with the introduction of no KYC, basic KYC, advanced KYC, and culminating in the highest performance achieved through a combination of advanced KYC and circular strategies.

A comparative analysis of agentic recommendation systems leveraging multimodal fusion, cross-domain learning, and social graph data to improve performance metrics such as nDCG and user surprise.

Traditional recommendation systems often struggle to personalize experiences beyond basic user profiles, limiting their effectiveness in diverse content domains. This is addressed in ‘An Comparative Analysis about KYC on a Recommendation System Toward Agentic Recommendation System’, which investigates the impact of incorporating detailed Know Your Customer (KYC) data and agentic AI within a multimodal recommendation framework. Results demonstrate that leveraging rich user context, social graphs, and cross-domain learning significantly enhances recommendation accuracy, diversity, and serendipity-as measured by nDCG-across advertising, news, and user-generated content. Could this approach pave the way for truly intelligent recommendation systems capable of anticipating and satisfying evolving user needs?

Beyond Immediate Gratification: Reclaiming the Value of Discovery

Contemporary recommendation systems are frequently optimized for immediate user interaction, with a heavy emphasis on maximizing click-through rate as the primary performance indicator. This focus, however, often comes at the expense of fostering genuine user satisfaction and enabling the discovery of diverse content. While effective at predicting what a user is likely to click on now, these systems frequently fail to consider long-term engagement or the potential for introducing users to novel and valuable items outside their existing preferences. The consequence is a cycle of reinforcing existing biases and limiting exposure to content that might be genuinely beneficial, even if it doesn’t immediately align with past behavior. This prioritization of short-term metrics can therefore inadvertently create a less enriching and more predictable user experience, hindering true exploration and the development of broader interests.

Recommendation systems, while adept at predicting immediate preferences, frequently confine users within self-reinforcing “filter bubbles”. These algorithmic echo chambers prioritize content aligning with existing tastes, inadvertently limiting exposure to diverse perspectives and novel information. This phenomenon isn’t merely about missing out on enjoyable discoveries; it actively hinders genuine exploration and intellectual growth. By continuously serving up what a user already likes, the system reduces the opportunity for serendipitous encounters with valuable content they might not have otherwise sought, ultimately diminishing the potential for broadening horizons and fostering new interests. The consequence is a diminished user experience where predictability replaces the thrill of discovery and long-term satisfaction is sacrificed for short-term engagement metrics.

Conventional recommendation techniques, such as collaborative filtering and content similarity, encounter significant obstacles when dealing with new items or users with limited interaction histories – a challenge known as the ‘cold-start’ problem. Collaborative filtering relies on patterns of user behavior, making it ineffective when faced with items lacking sufficient ratings or users with sparse profiles. Content similarity, while able to suggest items based on their attributes, struggles to introduce genuinely novel content, often reinforcing existing preferences rather than expanding horizons. This inherent limitation restricts the adaptability of these systems, hindering their ability to cater to evolving user tastes and discoverability of less popular, yet potentially valuable, content. Consequently, reliance on these methods can lead to a stagnant recommendation landscape, failing to fully leverage the breadth of available information and user potential.

Orchestrating Experiences: The Agentic Approach to Recommendation

The Agentic Recommendation System represents a departure from traditional recommendation models by conceptualizing the process as an intelligent agent actively planning a sequence of content for a user. Instead of simply predicting the next item a user might like, this system focuses on constructing a complete content journey. This is achieved by framing recommendation as a sequential decision-making problem, where the agent considers not only immediate relevance but also the overall trajectory of the user’s experience. The agent dynamically adapts this plan based on user interactions, aiming to maximize long-term engagement and satisfaction through a curated, multi-stage content pathway. This approach moves beyond item-to-item prediction to a holistic orchestration of content consumption.

The Agentic Recommendation System employs a multi-stage architecture utilizing Two-Tower Neural Networks to optimize content retrieval and presentation. This architecture separates the recommendation process into distinct stages: a recall stage, where candidate content is efficiently identified using the Two-Tower model’s ability to compute embeddings for users and items, and a ranking stage, where these candidates are scored and ordered based on predicted relevance. The Two-Tower model consists of two neural networks – one for user representation and one for content representation – trained to produce embeddings that facilitate fast similarity searches during recall. By decoupling user and item embeddings, the system scales efficiently to large catalogs and user bases, reducing computational cost compared to single-model approaches and enabling rapid identification of potentially relevant content before more complex ranking algorithms are applied.

The agentic recommendation system incorporates a ‘Global Logic’ layer to manage long-term content strategy, moving beyond immediate relevance to consider user goals and sustained engagement. This layer operates by defining abstract states representing user interests and formulating plans-sequences of content-to transition between those states. To prevent the system from converging solely on known preferences, ‘Exploration Rewards’ are integrated into the reward function; these incentivize the agent to select content with high uncertainty, effectively balancing exploitation of established user profiles with the discovery of potentially novel and relevant items. The magnitude of exploration rewards is dynamically adjusted to maintain an optimal balance between these competing objectives, ensuring continued learning and adaptation.

Enriching Understanding: Contextualizing Content and User Intent

The system utilizes Multimodal Fusion to generate a comprehensive content representation by aligning Visual Semantic Embedding with textual data. Visual Semantic Embedding translates image content into a vector space where semantically similar images are located close to each other, capturing visual features and their associated meanings. This embedding is then correlated with textual data – including descriptions, tags, and associated metadata – using techniques like cross-modal attention mechanisms. This alignment process enables the system to understand the relationship between visual and textual elements, creating a richer and more nuanced understanding of content than would be possible with either modality alone. The resulting holistic representation improves content indexing, retrieval, and relevance scoring for downstream tasks.

Social Graph Integration utilizes Graph Neural Networks (GNNs) to model relationships between users and their connections. These GNNs process user interaction data-such as follows, shares, and likes-represented as a graph structure where nodes are users and edges represent relationships. By learning embeddings for each node that capture both individual attributes and network position, the system infers contextual information about user preferences beyond explicit data. This enriched understanding of social connections enables more relevant recommendations, as the system can leverage the preferences of a user’s network to predict their interests. The GNNs allow for efficient propagation of information across the graph, identifying patterns and similarities that would be difficult to detect through traditional collaborative filtering methods.

Traditional Know Your Customer (KYC) processes, typically static data collection for compliance, are reframed within our system as a continually updated memory and context module. This module doesn’t simply store demographic or regulatory information; it actively learns and integrates user preferences, stated goals, and interaction history. By treating KYC data as a dynamic component of the agent’s memory, the system can personalize responses, anticipate user needs, and refine recommendations with increased accuracy. This approach moves beyond basic identification to provide a richer, behavioral understanding of each user, enhancing the agent’s ability to deliver relevant and effective assistance.

Addressing the Limits of Prediction: Cultivating Discovery and Serendipity

A significant challenge in recommendation systems lies in the ‘Cold Start’ problem – effectively serving new users with limited interaction history. This research addresses this by fusing contextual understanding with the agent’s inherent planning abilities. Rather than relying solely on past behavior, the system analyzes available contextual signals – such as time of day, current trends, or broad user demographics – and integrates them into the recommendation process. This allows the agent to proactively formulate plans that anticipate user needs, even without prior data. Consequently, relevant recommendations are generated for new users, fostering immediate engagement and circumventing the typical limitations of cold-start scenarios. This approach doesn’t simply offer a recommendation; it strategically proposes content aligned with the user’s likely intent, given the current context.

The system actively cultivates discovery through a carefully balanced approach to content recommendation. Rather than solely prioritizing items predicted to be of immediate interest, it incorporates ‘Exploration Rewards’ – incentives for the agent to suggest content diverging from established user preferences. This is further refined by ‘Global Logic’, which considers broader patterns and relationships within the content catalog, identifying unexpected connections that might resonate with the user. This dual mechanism ensures that recommendations aren’t confined to predictable patterns, instead presenting novel and potentially valuable content that users might not otherwise encounter, fostering a sense of serendipity and expanding their horizons within the platform.

Evaluation employed Normalized Discounted Cumulative Gain (nDCG) to rigorously assess the system’s ranking performance, revealing substantial improvements over baseline methods. The implemented approach achieved an nDCG@1 score of 0.652, representing nearly a 4.4-fold increase in ranking quality. This enhancement was particularly evident within the Sharing category, where an nDCG@5 score of 0.724 demonstrated a strong ability to surface highly relevant content, indicating a significant positive impact on user engagement and content discovery through optimized recommendation strategies.

Towards Proactive Guidance: The Future of Recommendation Systems

The advent of agentic recommendation systems signifies a shift from passive information retrieval to proactive, personalized guidance. These systems don’t merely present items based on past behavior; instead, they construct individualized learning pathways and dynamically curate content in response to a user’s evolving needs and goals. By modeling user preferences as an ongoing process-rather than a static profile-the system continuously refines its understanding and adjusts recommendations accordingly. This adaptive approach moves beyond simple prediction, fostering a more engaging and effective experience where content isn’t just relevant, but actively supports growth and discovery, tailoring itself to the user’s journey in real-time and ensuring sustained engagement with the platform.

The future of recommendation systems lies in their ability to synthesize information across disparate sources, and research is actively pursuing cross-domain fusion to achieve this. This involves moving beyond algorithms trained on single content silos – such as only music or only video – to create agents capable of intelligently connecting information from diverse areas like news, educational resources, and creative content. Such a system wouldn’t simply suggest similar items within a single domain, but rather build holistic recommendations based on a user’s evolving interests and needs, drawing connections between seemingly unrelated content. This approach promises a far more enriching and personalized experience, offering serendipitous discoveries and fostering deeper engagement by presenting information in unexpected, yet relevant, contexts.

Future development centers on leveraging multi-task learning, a technique allowing the recommendation system to simultaneously optimize for diverse and sometimes competing goals. Architectures like Mixture-of-Experts (MMoE) will be crucial in this endeavor, enabling specialized ‘expert’ networks within the system to focus on specific objectives – such as maximizing user engagement, improving long-term retention, and crucially, prioritizing user well-being. This approach moves beyond simple click-through rate optimization, aiming instead for holistic recommendations that cater to a user’s evolving needs and promote a positive experience. By treating these objectives not as separate problems, but as interconnected facets of a unified goal, the system can deliver more relevant, sustainable, and beneficial content recommendations.

The pursuit of increasingly sophisticated recommendation systems, as detailed in this analysis, hinges on a holistic understanding of user context. This work demonstrates that incorporating detailed user profiles-through methods like Know Your Customer (KYC)-and multimodal data significantly enhances performance. It recalls John McCarthy’s observation, “Every worthwhile task is invariably joined with some period of unpleasantness.” The initial effort to gather and integrate such extensive data – the ‘unpleasantness’ – is justified by the resultant improvements in accuracy, diversity, and serendipity. Just as a complex system requires understanding of all its parts, these agentic systems demand a complete picture of the user to truly deliver relevant and surprising recommendations.

Beyond the Horizon

The demonstrated gains from incorporating detailed user context-what has been termed ‘Know Your Customer’-into agentic recommendation systems are not merely incremental improvements, but rather indicative of a fundamental shift in how these systems must be conceived. The architecture dictates the behavior, and a system blind to the nuances of the user is, by definition, incomplete. However, the pursuit of ever-finer-grained user profiles carries inherent risks; the line between personalization and intrusive surveillance remains precariously thin, and the system’s response to evolving ethical boundaries will prove crucial.

Furthermore, while the confluence of multimodal data and social graphs demonstrably enhances performance metrics like nDCG and serendipity, the true complexity lies in the interaction of these elements. Simply fusing more data streams does not guarantee understanding. The system’s ability to learn and adapt across domains, to identify emergent patterns, and to avoid the echo chambers inherent in social networks, represents a substantial and largely unexplored challenge. Modifying one component inevitably triggers a cascade of effects throughout the entire structure.

Future work must therefore move beyond the optimization of individual algorithms and focus on the systemic properties of agentic systems. The emphasis should not be on what the system recommends, but on how it learns, adapts, and ultimately, co-evolves with the user. The pursuit of ever-increasing accuracy must be tempered by a recognition that a truly intelligent system is not merely a predictive engine, but a reflective one.

Original article: https://arxiv.org/pdf/2512.23961.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/