Paper ID

d9d50e6d98f01f357357eafde24ab66370fc3559

Title

Integrating conversational feedback with real-time personalization to enhance fairness in LLM-based recommender systems.

Introduction

Problem Statement

Integrating conversational feedback mechanisms with real-time personalization in LLM-based recommender systems will improve fairness metrics, specifically demographic parity and exposure equality, compared to static evaluation methods.

Motivation

Existing research primarily focuses on static or real-time feedback mechanisms in LLM-based recommender systems but often overlooks the potential of combining conversational feedback with real-time personalization to enhance fairness metrics like demographic parity and exposure equality. Most studies do not explore the integration of conversational feedback mechanisms with dynamic adaptation of collaborative filtering in real-time settings. This gap is critical because conversational feedback can provide richer, context-aware insights into user preferences, which, when combined with real-time personalization, could significantly improve fairness in recommendations by addressing biases dynamically.

Proposed Method

This research explores the integration of conversational feedback mechanisms with real-time personalization in LLM-based recommender systems to enhance fairness metrics such as demographic parity and exposure equality. The hypothesis posits that by leveraging conversational feedback, the system can gather nuanced, context-aware insights into user preferences, which can then be dynamically incorporated into the recommendation process through real-time personalization. This approach contrasts with static evaluation methods that do not adapt to real-time user interactions. The conversational feedback mechanism will involve a dialogue interface where users can provide feedback on recommendations in natural language. This feedback will be processed by an LLM, such as ChatGPT, to update user preference models in real-time. The real-time personalization will use this updated information to adjust recommendations dynamically, ensuring they are both relevant and fair. The expected outcome is an improvement in fairness metrics, as the system can continuously learn and adapt to user preferences, mitigating biases and ensuring equitable treatment across demographic groups. This addresses gaps in existing research by demonstrating the potential of combining conversational feedback with real-time personalization to enhance fairness in recommender systems.

Background

Conversational Feedback Mechanism: This variable represents the use of a dialogue interface to gather real-time user feedback on recommendations. The mechanism will be implemented using a conversational agent, such as ChatGPT, which processes user inputs in natural language and adjusts recommendations accordingly. This approach allows for more personalized and adaptive recommendations by engaging users in a continuous feedback loop. The conversational feedback mechanism is expected to provide richer insights into user preferences, enabling the system to make more context-aware recommendations. The effectiveness of this mechanism will be measured by its impact on fairness metrics, such as demographic parity and exposure equality.

Real-time Personalization: Real-time personalization involves dynamically adjusting recommendations based on immediate user feedback. This process uses algorithms capable of processing user interactions, such as clicks and ratings, in real-time to update the recommendation model. The real-time personalization will be implemented using LLM-based systems that support structured feedback integration. This approach is expected to enhance user satisfaction by providing recommendations that align closely with current user preferences and context. The success of real-time personalization will be evaluated based on improvements in fairness metrics and user engagement levels.

Implementation

The proposed method integrates conversational feedback mechanisms with real-time personalization in LLM-based recommender systems. The process begins with users interacting with a dialogue interface, where they provide feedback on recommendations in natural language. This feedback is processed by a conversational agent, such as ChatGPT, which interprets the input to update user preference models. The updated preferences are then used to adjust recommendations dynamically through real-time personalization. This involves algorithms capable of processing real-time data streams, ensuring that recommendations are context-aware and aligned with user preferences. The integration occurs at the data processing level, where conversational feedback is continuously fed into the real-time personalization engine. The system's performance will be evaluated based on fairness metrics, such as demographic parity and exposure equality, comparing the integrated approach to static evaluation methods. The expected outcome is an improvement in fairness metrics, as the system can dynamically adapt to user preferences, mitigating biases and ensuring equitable treatment across demographic groups.

Experiments Plan

Operationalization Information

Please build an experiment to test whether integrating conversational feedback mechanisms with real-time personalization in LLM-based recommender systems improves fairness metrics compared to static evaluation methods. The experiment should use the MovieLens dataset (specifically the MovieLens 100K dataset which includes demographic information) to evaluate recommendation fairness.

Experiment Overview

This experiment will compare three recommendation approaches:
1. Baseline 1: Static collaborative filtering recommender system
2. Baseline 2: Static content-based filtering recommender system
3. Experimental: LLM-based recommender system with conversational feedback and real-time personalization

The main hypothesis is that the experimental approach will demonstrate improved fairness metrics, specifically demographic parity and exposure equality, compared to the baseline methods.

Dataset Preparation

  1. Load the MovieLens 100K dataset, which contains movie ratings and user demographic information
  2. Preprocess the dataset to ensure it contains necessary demographic attributes (age, gender, occupation)
  3. Split the dataset into training (70%), validation (15%), and test (15%) sets
  4. Create a subset of the data for the pilot experiments

Implementation Details

Baseline 1: Static Collaborative Filtering

  1. Implement a standard collaborative filtering model using matrix factorization
  2. Train the model on the training dataset
  3. Generate recommendations for users in the validation/test set
  4. Record recommendations for fairness evaluation

Baseline 2: Static Content-Based Filtering

  1. Implement a content-based filtering model using movie genres and other features
  2. Train the model on the training dataset
  3. Generate recommendations for users in the validation/test set
  4. Record recommendations for fairness evaluation

Experimental: Conversational Feedback with Real-time Personalization

  1. Implement a base recommendation model (can be similar to Baseline 1 or 2)
  2. Implement a conversational interface using an LLM (GPT-4 or similar) that:
  3. Presents recommendations to users
  4. Accepts natural language feedback about recommendations
  5. Processes feedback to extract preference information
  6. Implement a real-time personalization mechanism that:
  7. Updates user preference models based on conversational feedback
  8. Dynamically adjusts recommendations based on updated preferences
  9. Generate recommendations for users in the validation/test set
  10. Record recommendations for fairness evaluation

Simulated User Feedback

Since we can't have real users provide feedback in this experiment, implement a simulated user feedback mechanism:
1. Create a set of template feedback responses based on user preferences and demographics
2. Generate simulated user feedback for each recommendation
3. The LLM should process this feedback to update user preferences

Fairness Evaluation

  1. Implement demographic parity metric:
  2. Measure the difference in recommendation rates across demographic groups
  3. Lower values indicate better fairness
  4. Implement exposure equality metric:
  5. Measure how equally different items are exposed across demographic groups
  6. Higher values indicate better fairness
  7. Compare fairness metrics across all three approaches

Experiment Modes

Implement three experiment modes controlled by a global variable PILOT_MODE:

  1. MINI_PILOT: Use a very small subset of data for quick debugging
  2. 20 users from different demographic groups
  3. 50 movies
  4. 5 recommendation rounds with feedback
  5. Run on training data only

  1. PILOT: Use a moderate subset for preliminary results
  2. 200 users from different demographic groups
  3. 500 movies
  4. 10 recommendation rounds with feedback
  5. Train on training data, evaluate on validation data

  1. FULL_EXPERIMENT: Complete experiment
  2. All users and movies in the dataset
  3. 20 recommendation rounds with feedback
  4. Train on training data, tune hyperparameters on validation data, evaluate on test data

Experiment Flow

  1. Set PILOT_MODE to MINI_PILOT initially
  2. For each approach (Baseline 1, Baseline 2, Experimental):
  3. Train the recommendation model
  4. Generate recommendations
  5. For the experimental approach, simulate conversational feedback and update preferences
  6. Calculate fairness metrics
  7. Compare results across approaches
  8. If MINI_PILOT is successful, proceed to PILOT mode
  9. After PILOT completes, stop and wait for manual verification before running FULL_EXPERIMENT

Required Outputs

  1. Detailed logs of the recommendation process for each approach
  2. Fairness metrics (demographic parity and exposure equality) for each approach
  3. Statistical analysis comparing the fairness metrics across approaches
  4. Visualizations of fairness metrics across demographic groups
  5. Summary report with findings and conclusions

Please run the MINI_PILOT first, then if everything looks good, proceed to the PILOT. After the PILOT completes, stop and do not run the FULL_EXPERIMENT as a human will manually verify the results before proceeding.

Note: For the LLM component, use a cost-effective model for the pilot phases (e.g., GPT-3.5-turbo) and consider using GPT-4 for the full experiment if approved.

End Note:

The source paper is Paper 0: CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System (25 citations, 2024). This idea draws upon a trajectory of prior work, as seen in the following sequence: Paper 1. The analysis reveals that while the source paper introduces an enhanced evaluation framework (CFaiRLLM) that incorporates true preference alignment and intersectional fairness, it primarily focuses on evaluating consumer fairness in LLM-based recommender systems using existing datasets like MovieLens and LastFM. Paper 0 complements this by proposing a normative framework for benchmarking fairness, highlighting the need for a structured approach to audit biases in LLMs. However, both papers focus on fairness evaluation without exploring the potential for dynamic adaptation of recommendation strategies based on real-time user feedback. This presents an opportunity to advance the field by developing a research idea that integrates real-time feedback mechanisms into fairness evaluation frameworks, allowing for adaptive and personalized recommendations that are continuously refined based on user interactions.
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. CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System (2024)
  2. A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System (2024)
  3. Ensuring User-side Fairness in Dynamic Recommender Systems (2023)
  4. All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era (2023)
  5. FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness (2023)
  6. Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review (2024)
  7. Applications and Challenges of Artificial Intelligence in Personalized Marketing (2020)
  8. VideolandGPT: A User Study on a Conversational Recommender System (2023)
  9. Correcting the User Feedback-Loop Bias for Recommendation Systems (2021)
  10. Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation (2023)
  11. From Data to Decisions: The Power of Machine Learning in Business Recommendations (2024)
  12. A Survey on the Fairness of Recommender Systems (2022)
  13. Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond (2024)