Combining Conversational Recommender Systems and Large Language Models for Effective E-commerce Pre-sales Dialogues

แนวคิดหลัก

Combining Large Language Models (LLMs) with Conversational Recommender Systems (CRSs) can significantly improve their individual performance in understanding and responding to user needs within e-commerce pre-sales dialogues.

บทคัดย่อ

• Bibliographic Information: Liu, Y., Zhang, W.-N., Chen, Y., Zhang, Y., Bai, H., Feng, F., Cui, H., Li, Y., & Che, W. (2024). Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue. arXiv preprint arXiv:2310.14626v2.
• Research Objective: This paper investigates the effectiveness of combining LLMs and CRSs in e-commerce pre-sales dialogues to enhance their ability to understand user needs and provide accurate recommendations.
• Methodology: The authors propose two collaboration methods: "CRS assisting LLM" and "LLM assisting CRS." They evaluate these methods on four tasks: pre-sales dialogue understanding, user needs elicitation, user needs-based recommendation, and pre-sales dialogue generation. The experiments are conducted on the U-NEED dataset, a real-world dataset of e-commerce pre-sales dialogues.
• Key Findings: The results demonstrate that combining LLMs and CRSs leads to significant improvements in pre-sales dialogue understanding, user needs elicitation, and user needs-based recommendation. Notably, LLMs benefit from the domain-specific knowledge of CRSs, while CRSs leverage the language understanding and generation capabilities of LLMs. However, the impact on pre-sales dialogue generation is marginal, possibly due to the similar approaches of LLMs and the chosen CRS (UniMIND) in generating responses.
• Main Conclusions: The study concludes that combining LLMs and CRSs holds significant potential for enhancing e-commerce pre-sales dialogues. The authors suggest that future research should explore collaborations between LLMs and CRSs that focus on generating persuasive reasons for recommendations.
• Significance: This research contributes to the field of conversational AI by demonstrating the benefits of combining LLMs and CRSs for improved user experience in e-commerce.
• Limitations and Future Research: The study is limited to LLMs with around 7B parameters and a specific CRS architecture. Future research could explore the impact of different LLM sizes and CRS architectures on the collaboration effectiveness. Additionally, investigating collaborations across different product categories to provide comprehensive recommendations is a promising direction.

สถิติ

BCRS-CLLM achieves a 12.3% improvement over ChatGLM on the average F1 score across all 5 categories for user need elicitation.
ALLM-CCRS achieves 6.9%, 3.8%, and 4.9% improvements on Accuracy, Hit@5, and MRR@5, respectively, compared to UniMIND (CPT) for user needs-based recommendation.
ALLM-BCRS achieves 9.4%, 3.0%, and 6.0% improvements on Accuracy, Hit@5, and MRR@5, respectively, when compared to UniMIND(BART) for user needs-based recommendation.

คำพูด

ข้อมูลเชิงลึกที่สำคัญจาก

Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue

by Yuanxing Liu... ที่ arxiv.org 10-21-2024

https://arxiv.org/pdf/2310.14626.pdf

สอบถามเพิ่มเติม

How can the proposed collaboration methods be adapted for other e-commerce tasks, such as customer support or post-sales interactions?

The collaboration methods of "CRS assisting LLM" and "LLM assisting CRS" proposed in the paper demonstrate strong adaptability and can be effectively applied to other e-commerce tasks like customer support and post-sales interactions. Here's how:
1. Customer Support:

LLM assisting CRS: LLMs can be used to analyze customer queries, understand intent (e.g., return, technical issue, order status), and categorize them. This information can be fed to the CRS to retrieve relevant solutions, FAQs, or route the customer to the appropriate support agent.
CRS assisting LLM: The CRS can maintain a knowledge base of product information, common issues, and solutions. Based on the LLM's understanding of the customer's problem, the CRS can provide targeted information to the LLM, enabling it to generate more accurate and helpful responses.
2. Post-Sales Interactions:

LLM assisting CRS: LLMs can be used to analyze customer reviews, identify potential issues with products, and gauge customer satisfaction. This information can be used by the CRS to recommend relevant solutions, offer proactive support, or suggest complementary products.
CRS assisting LLM: Based on a customer's purchase history and the LLM's analysis of their post-purchase behavior, the CRS can recommend personalized offers, loyalty programs, or provide relevant product care instructions through the LLM.
Adaptations for Specific Tasks:
While the core principles of collaboration remain consistent, certain adaptations might be necessary:

Data Fine-tuning: LLMs and CRSs would need to be fine-tuned on datasets specific to customer support or post-sales interactions. This ensures they are familiar with the language, terminology, and common scenarios encountered in these contexts.
Integration with E-commerce Platforms: Seamless integration with existing e-commerce platforms is crucial for accessing customer data, order history, product information, and other relevant details.
Evaluation Metrics: Evaluation metrics should be tailored to the specific task. For customer support, metrics like first-contact resolution rate and customer satisfaction scores are relevant. For post-sales, metrics like repeat purchase rate and customer lifetime value are important.

Could the reliance on large language models introduce biases into the recommendations provided by the system, and how can these biases be mitigated?

Yes, the reliance on large language models (LLMs) can introduce biases into the recommendations provided by e-commerce systems. This is because LLMs are trained on massive datasets that can contain societal biases related to gender, race, age, or other sensitive attributes. These biases can manifest in various ways:

Product Recommendations: LLMs might recommend products that reinforce stereotypes, such as associating certain toys with boys and others with girls.
Language Used: The language used by LLMs in recommendations might perpetuate biases, like using more assertive language when recommending products to men compared to women.
Personalized Offers: LLMs might offer different discounts or promotions based on biased assumptions about a customer's purchasing power or preferences.
Mitigating Biases:
Addressing biases in LLM-powered recommendation systems requires a multi-faceted approach:

Data Bias Mitigation:

Dataset Auditing: Thoroughly audit training datasets to identify and quantify existing biases.
Data Balancing: Employ techniques to balance the representation of different demographic groups in the training data.
Counterfactual Data Augmentation: Generate synthetic data points that counter existing biases in the dataset.

Model Training and Design:

Adversarial Training: Train the LLM to recognize and mitigate biased patterns in the data.
Fairness Constraints: Incorporate fairness constraints into the LLM's objective function to penalize biased recommendations.
Explainable Recommendations: Design the system to provide explanations for its recommendations, making it easier to identify and address potential biases.

Human Oversight and Evaluation:

Human-in-the-Loop: Incorporate human review and feedback mechanisms, especially for sensitive recommendations.
Continuous Monitoring: Regularly monitor the system's recommendations for biases and make necessary adjustments.

Ethical Guidelines and Regulations:

Industry Standards: Develop and adhere to ethical guidelines for AI-powered recommendation systems.
Regulatory Frameworks: Advocate for and comply with regulations that address bias and fairness in AI systems.

What are the ethical implications of using AI-powered systems to influence customer decisions in e-commerce settings, and how can these concerns be addressed?

The use of AI-powered systems in e-commerce to influence customer decisions raises several ethical concerns:

Manipulation and Exploitation: AI systems can be used to exploit vulnerabilities in human psychology, nudging customers towards purchases they might not otherwise make. This raises concerns about manipulation and the erosion of free will.
Privacy Violation: AI systems often rely on vast amounts of personal data to personalize recommendations. This raises concerns about privacy violations, especially if data is collected or used without informed consent.
Discrimination and Fairness: As discussed earlier, AI systems can perpetuate existing biases, leading to unfair or discriminatory outcomes for certain customer groups.
Transparency and Accountability: The decision-making processes of complex AI systems can be opaque, making it difficult to understand why certain recommendations are made. This lack of transparency hinders accountability and makes it challenging to address potential harms.
Addressing Ethical Concerns:

Transparency and Explainability: Develop AI systems that provide clear explanations for their recommendations, enabling customers to understand the reasoning behind suggestions.
User Control and Empowerment: Give customers greater control over their data and the recommendations they receive. Allow them to opt-out of personalized recommendations or adjust their preference settings.
Fairness and Non-Discrimination: Implement measures to ensure that AI systems do not discriminate against any customer group. Regularly audit and monitor systems for bias.
Privacy Protection: Prioritize data privacy and security. Collect and use customer data responsibly and transparently, obtaining informed consent whenever necessary.
Ethical Guidelines and Regulations: Establish clear ethical guidelines for the development and deployment of AI-powered e-commerce systems. Advocate for and comply with relevant regulations.
Public Discourse and Education: Foster open discussions about the ethical implications of AI in e-commerce. Educate consumers about how these systems work and empower them to make informed decisions.
By proactively addressing these ethical concerns, we can harness the power of AI in e-commerce while ensuring fairness, transparency, and respect for customer autonomy.