Responsible and Efficient Adaptation of Large Language Models to Improve Robustness of Recommendation Systems for Diverse User Populations

Centrala begrepp

A hybrid framework that leverages the capabilities of both traditional recommendation systems and large language models to improve the robustness of recommendations for diverse user populations, especially those with sparse interaction histories.

Sammanfattning

The paper proposes a hybrid framework that combines the strengths of traditional recommendation systems (RSs) and large language models (LLMs) to improve the robustness of recommendations for diverse user populations. The key aspects of the framework are:

Identifying "weak" users: The framework first identifies users for whom the traditional RS performs poorly, based on two criteria - the sparsity of their interaction history and the ranking performance (measured using AUC) of the RS on their preferences.

Contextualizing weak user preferences: For the identified weak users, the framework uses in-context learning to contextualize their interaction histories as distinct ranking tasks and provides them as instructions to the LLM.

Hybrid task allocation: While the strong users (those with dense interaction histories and good RS performance) receive recommendations from the traditional RS, the weak users receive recommendations ranked by the LLM if the LLM outperforms the RS.

The experiments on three real-world datasets (MovieLens-1M, MovieLens-100k, and Book-Crossing) show that the proposed framework can significantly reduce the count of weak users (by up to 87% using GPT-3.5-turbo and 85% using Mixtral-8x7b-instruct) and improve the overall robustness of the recommendations (by around 12%) without disproportionately escalating the costs associated with adapting LLMs.

Statistik

The sparsity of the MovieLens-1M dataset is 95.81%, the MovieLens-100k dataset is 93.7%, and the Book-Crossing dataset is 99.82%.

Citat

"Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples. This makes it hard for data-driven RSs to cater to a diverse set of users due to the varying properties of these users."
"While recent works have shown promising results in adapting large language models (LLMs) for recommendation to address hard samples, long user queries from millions of users can degrade the performance of LLMs and elevate costs, processing times and inference latency."

Viktiga insikter från

Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations

by Kirandeep Ka... på arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00824.pdf

Djupare frågor

How can the proposed framework be extended to incorporate user demographic information, in addition to interaction history, to further improve the robustness of recommendations for marginalized user groups?

Incorporating user demographic information alongside interaction history can significantly enhance the robustness of recommendations for marginalized user groups within the proposed framework. By integrating demographic data such as age, gender, location, and preferences, the system can tailor recommendations more effectively to meet the diverse needs of users. This extension can be achieved through the following steps:

Data Integration: Collect and integrate demographic information into the existing user profiles within the recommendation system. This data can be obtained through user registration, surveys, or inferred from user behavior.

Segmentation: Utilize demographic data to segment users into distinct groups based on common characteristics. This segmentation allows for personalized recommendations that cater to the specific preferences of each group.

Contextualization: Contextualize user interactions and preferences with demographic attributes to create a more comprehensive understanding of user behavior. This combined context can provide deeper insights into user preferences and needs.

Task Allocation: Modify the task allocation strategy to consider both interaction history and demographic information when identifying weak users. By incorporating demographic factors, the system can better identify marginalized user groups that may require specialized recommendations.

Prompting Strategies: Develop prompting strategies that incorporate demographic cues to guide the LLM in generating personalized recommendations. These prompts can include demographic attributes to provide a more nuanced understanding of user preferences.

Evaluation Metrics: Adjust evaluation metrics to assess the performance of the system on marginalized user groups. By including demographic factors in the evaluation process, the system can measure its effectiveness in catering to diverse user segments.

By extending the framework to incorporate user demographic information, the recommendation system can offer more tailored and inclusive recommendations, ultimately improving the overall user experience for marginalized user groups.

How can the proposed framework be extended to incorporate user demographic information, in addition to interaction history, to further improve the robustness of recommendations for marginalized user groups?

Incorporating user demographic information alongside interaction history can significantly enhance the robustness of recommendations for marginalized user groups within the proposed framework. By integrating demographic data such as age, gender, location, and preferences, the system can tailor recommendations more effectively to meet the diverse needs of users. This extension can be achieved through the following steps:

Data Integration: Collect and integrate demographic information into the existing user profiles within the recommendation system. This data can be obtained through user registration, surveys, or inferred from user behavior.

Segmentation: Utilize demographic data to segment users into distinct groups based on common characteristics. This segmentation allows for personalized recommendations that cater to the specific preferences of each group.

Contextualization: Contextualize user interactions and preferences with demographic attributes to create a more comprehensive understanding of user behavior. This combined context can provide deeper insights into user preferences and needs.

Task Allocation: Modify the task allocation strategy to consider both interaction history and demographic information when identifying weak users. By incorporating demographic factors, the system can better identify marginalized user groups that may require specialized recommendations.

Prompting Strategies: Develop prompting strategies that incorporate demographic cues to guide the LLM in generating personalized recommendations. These prompts can include demographic attributes to provide a more nuanced understanding of user preferences.

Evaluation Metrics: Adjust evaluation metrics to assess the performance of the system on marginalized user groups. By including demographic factors in the evaluation process, the system can measure its effectiveness in catering to diverse user segments.

By extending the framework to incorporate user demographic information, the recommendation system can offer more tailored and inclusive recommendations, ultimately improving the overall user experience for marginalized user groups.

How can the prompting strategies for LLMs be further refined to better capture the preferences of extremely weak users, for whom even the LLM may struggle to provide accurate recommendations?

Refining prompting strategies for LLMs to capture the preferences of extremely weak users is crucial for enhancing recommendation accuracy and user satisfaction. To address this challenge and improve the performance of LLMs on weak users, the following strategies can be implemented:

Personalized Prompts: Tailor prompts based on the specific characteristics and preferences of each weak user. By customizing the prompts to align with individual user needs, the LLM can better understand and generate relevant recommendations.

Contextual Information: Provide additional contextual information in prompts to offer a more comprehensive understanding of user preferences. Incorporating details such as past interactions, demographic data, and user behavior can help the LLM generate more accurate recommendations.

Multi-Modal Inputs: Integrate multi-modal inputs, such as text, images, and user feedback, into the prompting process. By leveraging diverse data sources, the LLM can capture a richer representation of user preferences and improve recommendation quality.

Dynamic Prompting: Implement dynamic prompting strategies that adapt based on user feedback and interaction patterns. By continuously updating prompts in real-time, the LLM can adjust its recommendations to better align with evolving user preferences.

Feedback Loop: Establish a feedback loop mechanism where users can provide input on the recommendations received. This feedback can be used to refine prompting strategies and enhance the LLM's understanding of user preferences over time.

Collaborative Filtering: Combine collaborative filtering techniques with prompting strategies to leverage user-item interactions for generating prompts. By incorporating collaborative filtering principles, the LLM can benefit from collective user behavior data to improve recommendation accuracy.

Transfer Learning: Apply transfer learning techniques to adapt prompts from similar user groups or domains with known preferences. By transferring knowledge from related contexts, the LLM can enhance its ability to capture the preferences of extremely weak users.

By refining prompting strategies with these approaches, the LLM can better capture the preferences of extremely weak users and provide more accurate and personalized recommendations, ultimately improving the overall recommendation performance for this user segment.