Paper ID

1828e31b0cfac45c3a616c78f2547fbe47548bf7

Title

Integrating explicit ratings, implicit behavior tracking, and real-time personalization in a multi-agent system to enhance recommendations.

Introduction

Problem Statement

Integrating explicit ratings, implicit behavior tracking, and real-time personalization in a multi-agent conversational recommender system will significantly enhance recommendation accuracy and user satisfaction compared to systems using static dialogue strategies.

Motivation

Existing conversational recommender systems often focus on either explicit user feedback (like ratings) or implicit behavior tracking (like clicks) but rarely explore the combined effect of these feedback mechanisms with real-time personalization in multi-agent frameworks. This gap is significant because while explicit feedback provides clear user preferences, implicit feedback captures nuanced user behaviors. The integration of both with real-time personalization could dynamically enhance recommendation accuracy and user satisfaction, yet this combination remains underexplored. Our hypothesis addresses this by testing the synergistic effect of explicit ratings, implicit behavior tracking, and real-time personalization within a multi-agent conversational recommender system framework.

Proposed Method

This research explores the impact of combining explicit ratings, implicit behavior tracking, and real-time personalization within a multi-agent conversational recommender system. The hypothesis is that this integration will improve recommendation accuracy and user satisfaction. Explicit ratings provide direct user feedback, allowing the system to adjust dialogue strategies and refine user profiles. Implicit behavior tracking captures user actions like clicks and dwell time, offering insights into user preferences without direct input. Real-time personalization dynamically adjusts recommendations based on evolving user feedback, ensuring that the system remains responsive to user needs. By leveraging these mechanisms in a multi-agent framework, the system can dynamically optimize recommendations, leading to improved precision and recall. This approach addresses the gap in existing research by combining feedback mechanisms that have traditionally been used in isolation, thus offering a novel method for enhancing user experience in conversational recommender systems.

Background

Explicit Ratings: Explicit ratings involve users providing direct feedback on recommendations, such as star ratings or thumbs up/down. This feedback is integrated into the system's memory module, allowing the system to adjust its dialogue strategies and recommendation algorithms dynamically. The explicit ratings are used to refine user profiles, which are then utilized by responder agents to generate more personalized responses in subsequent interactions. This mechanism helps create a higher-level understanding of user preferences, crucial for the information-level reflection process.

Implicit Behavior Tracking: Implicit behavior tracking captures user actions such as clicks, dwell time, and browsing history without requiring direct input from the user. This data is processed to infer user preferences and satisfaction levels, which are then used to adjust the dialogue act plan in real-time. The strategy-level reflection mechanism utilizes this implicit feedback to deduce reasons for recommendation failures and provide corrective experiences to the agents.

Real-Time Personalization: Real-time personalization involves adjusting recommendations and dialogue strategies based on user feedback captured during interactions. This approach uses structured feedback integration and engagement incentives to refine algorithms and adapt recommendations to changing user preferences. The system employs customizable settings and intuitive interfaces to capture user input, which is then used to enhance satisfaction and trust.

Implementation

The proposed method integrates explicit ratings, implicit behavior tracking, and real-time personalization within a multi-agent conversational recommender system. First, explicit ratings are collected from users through a user-friendly interface, which are then stored in the system's memory module. These ratings are used to update user profiles and adjust dialogue strategies. Simultaneously, implicit behavior tracking monitors user actions such as clicks and dwell time to infer preferences. This data is processed in real-time to adjust the dialogue act plan, ensuring that recommendations remain relevant. Real-time personalization dynamically adapts recommendations based on the combined feedback from explicit ratings and implicit behavior tracking. The multi-agent framework, consisting of responder and planner agents, leverages this feedback to generate personalized responses. The integration occurs at multiple levels: explicit ratings refine user profiles, implicit tracking informs real-time adjustments, and personalization ensures that the system remains responsive to user needs. The expected outcome is an improvement in recommendation accuracy and user satisfaction, as measured by precision, recall, and user survey scores.

Experiments Plan

Operationalization Information

Please build an experiment to test the hypothesis that integrating explicit ratings, implicit behavior tracking, and real-time personalization in a multi-agent conversational recommender system will significantly enhance recommendation accuracy and user satisfaction compared to systems using static dialogue strategies.

Dataset

Use the MovieLens dataset (specifically the MovieLens 100K dataset for the pilot experiments) which contains user ratings for movies. This dataset provides a rich source of user interactions and feedback that can be used to simulate user behavior and evaluate recommendation systems.

Experiment Structure

Implement a global variable PILOT_MODE with three possible settings: 'MINI_PILOT', 'PILOT', or 'FULL_EXPERIMENT'. The experiment should start with MINI_PILOT, then proceed to PILOT if successful, but stop before FULL_EXPERIMENT for human verification.

MINI_PILOT

PILOT

FULL_EXPERIMENT

Systems to Implement

  1. Baseline System 1: Static Dialogue Strategy
  2. Implement a recommender system with fixed dialogue patterns
  3. No adaptation based on user feedback
  4. Use collaborative filtering for recommendations
  5. Dialogue follows a predetermined script with minimal variation

  1. Baseline System 2: Explicit Ratings Only
  2. Implement a system that only uses explicit ratings (1-5 stars) from users
  3. Update user profiles based on these ratings
  4. Adjust recommendations based on explicit feedback only
  5. No implicit behavior tracking or real-time personalization

  1. Baseline System 3: Implicit Feedback Only
  2. Implement a system that only uses implicit feedback (clicks, dwell time, etc.)
  3. Infer user preferences from behavior without explicit ratings
  4. No explicit rating collection or real-time personalization

  1. Experimental System: Dynamic Feedback Integration
  2. Implement a multi-agent system with the following components:
    a. Memory Module: Stores user profiles, interaction history, and feedback
    b. Responder Agent: Generates personalized responses based on user profiles
    c. Planner Agent: Adjusts dialogue strategies based on feedback
  3. Integrate all three feedback mechanisms:
    a. Explicit Ratings: Collect star ratings (1-5) after recommendations
    b. Implicit Behavior Tracking: Track clicks, selection time, and browsing patterns
    c. Real-Time Personalization: Dynamically adjust recommendations during the conversation

Implementation Details

Multi-Agent Framework

  1. Implement a two-agent system with:
  2. Responder Agent: Responsible for generating personalized responses and recommendations
  3. Planner Agent: Responsible for dialogue strategy and adjusting the conversation flow

Explicit Ratings Integration

  1. After each recommendation, simulate user providing a rating (1-5 stars)
  2. Store these ratings in the memory module
  3. Update user profiles based on these ratings
  4. Use these profiles to inform future recommendations

Implicit Behavior Tracking

  1. Track simulated user actions:
  2. Click-through rate (whether user selects a recommended item)
  3. Selection time (how long it takes to make a selection)
  4. Browsing patterns (which items user views before making a selection)
  5. Process this data to infer user preferences
  6. Use a simple model to convert these actions into preference scores

Real-Time Personalization

  1. Implement a mechanism to adjust recommendations during the conversation
  2. Use both explicit and implicit feedback to update recommendations in real-time
  3. Adjust dialogue strategies based on inferred user satisfaction

User Simulation

  1. Create simulated users based on MovieLens profiles
  2. Implement behavior models that simulate:
  3. How users rate movies (based on their historical ratings)
  4. How users interact with recommendations (clicks, browsing)
  5. How user preferences evolve during a conversation

Evaluation Metrics

  1. Recommendation Accuracy:
  2. Precision: Proportion of recommended items that are relevant
  3. Recall: Proportion of relevant items that are recommended
  4. F1 Score: Harmonic mean of precision and recall
  5. NDCG (Normalized Discounted Cumulative Gain): Measures ranking quality

  1. User Satisfaction:
  2. Simulate user satisfaction scores based on:
    • Match between recommendations and known preferences
    • Conversation efficiency (how quickly user finds relevant items)
    • Diversity of recommendations

  1. Conversation Efficiency:
  2. Number of turns needed to reach satisfactory recommendations
  3. Proportion of successful conversations (where user finds relevant items)

Analysis

  1. Compare all systems on the metrics above
  2. Perform statistical significance testing (t-tests or bootstrap resampling)
  3. Analyze which components contribute most to performance improvements
  4. Generate visualizations showing:
  5. Performance comparison across systems
  6. Learning curves showing how systems improve over interactions
  7. Ablation analysis of different components

Output

  1. Generate a comprehensive report with:
  2. Experimental setup and methodology
  3. Results tables with all metrics
  4. Statistical significance analysis
  5. Visualizations of key findings
  6. Discussion of implications

  1. Save all experimental data including:
  2. Trained models
  3. Conversation logs
  4. User profiles
  5. Performance metrics at each step

Please implement this experiment starting with the MINI_PILOT configuration, then proceed to PILOT if successful, but stop before FULL_EXPERIMENT for human verification of results.

End Note:

The source paper is Paper 0: A Multi-Agent Conversational Recommender System (29 citations, 2024). This idea draws upon a trajectory of prior work, as seen in the following sequence: Paper 1 --> Paper 2. The analysis reveals that while the source paper and Paper 0 focus on improving user interaction and system performance through multi-agent frameworks, there is a gap in addressing the dynamic adaptation of dialogue strategies based on real-time user feedback. Paper 1 provides a broader framework perspective but lacks specific solutions to enhance user experience dynamically. A promising research direction would be to develop a system that not only optimizes latency and scalability but also dynamically adapts its dialogue strategies based on real-time user feedback, thus improving both the efficiency and personalization of recommendations.
The initial trend observed from the progression of related work highlights a consistent research focus. However, the final hypothesis proposed here is not merely a continuation of that trend — it is the result of a deeper analysis of the hypothesis space. By identifying underlying gaps and reasoning through the connections between works, the idea builds on, but meaningfully diverges from, prior directions to address a more specific challenge.

References

  1. A Multi-Agent Conversational Recommender System (2024)
  2. A Hybrid Multi-Agent Conversational Recommender System with LLM and Search Engine in E-commerce (2024)
  3. An Agentic AI-based Multi-Agent Framework for Recommender Systems (2024)
  4. Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models (2025)
  5. A Survey on Reinforcement Learning for Recommender Systems (2021)
  6. Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond (2024)
  7. Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (2018)
  8. User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations (2023)
  9. Improving GenIR Systems Based on User Feedback (2025)
  10. The Influence of UX Design on User Retention and Conversion Rates in Mobile Apps (2019)
  11. Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfaces (2025)