Leverage Large Language Models such as ChatGPT for your Ecommerce System
Published Mar 29 2024 02:02 PM 25K Views
Microsoft

ChatGPT Ecommerce System 

This solution guide presents a novel approach that leverages Large Language Models, such as ChatGPT, to address the limitations found in traditional recommendation methods. Traditional methods are typically task-specific and therefore require corresponding data to train distinct models for various applications. These methods often lack generalization capabilities and underperform in cold start situations. To overcome these challenges, we propose and implement an ecommerce recommender system based on ChatGPT. This system is specifically designed for recommendation systems in scenarios with limited task-specific data, for example, cold start problem that often associates with new users. The "cold start problem" is a term commonly used in the context of recommendation systems, machine learning, and data-driven applications to describe the difficulty of making accurate recommendations or decisions when there is very little data on users or items.

 

Additionally, using ChatGPT to do ecommerce product feature summarization, as well as product reviews generation is also included in this solution notebook, to demonstrate how to build an ecommerce ecosystem using ChatGPT. 

 

Features 

Use cases cover three areas:

1. Recommendation based on user-item interaction history.  

Pointwise Recommender Systems predict how relevant each item is to a user by scoring each one individually, like a regression problem.
Pairwise Recommender Systems compare two items at a time to see which one a user might prefer, focusing on learning rankings through item comparisons.
Listwise Recommender Systems consider all items together, aiming to order the entire list to align with a user's preferences, from most to least relevant.

2 Summarize product features, these summarizations can be used in email campaigns. 

3 Product review generation. Generation high level product reviews from lot of users' reviews.

 

System Architecture 

This diagram illustrates the e-commerce system outlined in the document. Upon initiation of a request or conversation, the router determines which of the three feature branches is best suited to handle the incoming request. For the recommendation branch, there are three approaches available, each contingent upon the type of information storage data utilized. The choice of approach and the corresponding data available influence the prompts employed. Ultimately, GPT is deployed to generate responses, thereby fulfilling the request.

 

eCommerce_Diagram_v3.png

 

 

Biases on LLM Ranking and How to Address Them 

Position Bias  

One of the challenges faced by LLMs is the order of candidates affects the ranking results of LLMs. While traditional ranking methods are not usually affected by the order of retrieved candidates, LLMs are known to be sensitive to the order of examples in NLP prompts. Specifically, it has been observed that the ranking performance drops significantly when the ground-truth items appear at the last few positions.

 

This phenomenon is known as Position Bias, which can be mitigated through the use of bootstrapping. This involves randomly assigning candidate items to different positions and repeating the ranking task several times. 

 

Popularity Bias 

Another form of bias that affects LLM ranking is Popularity Bias. Similar to conventional recommender systems, LLMs tend to prioritize more popular items and rank them higher. To reduce Popularity Bias, LLMs can be designed to focus more on historical interactions rather than relying on popularity. It has been observed that the more historical interactions available, the less the output is influenced by popularity. 

Reference: [2305.08845] Large Language Models are Zero-Shot Rankers for Recommender Systems (arxiv.org) 

 

 3 types of GPT Recommender

  • Pointwise Recommender Systems: 
  • These systems evaluate each item individually to predict its relevance to a user. The approach treats the recommendation task as a regression problem, where the goal is to predict a score or probability indicating how likely a user is to be interested in each item. 
     
  • Pairwise Recommender Systems: 
  • Pairwise systems focus on comparing pairs of items to determine which one is more preferable or relevant to the user. This method is about learning preferences and rankings by comparing items in pairs, rather than scoring them independently. 
     
  • Listwise Recommender Systems: 
  • Listwise Recommender Systems go beyond individual or pairwise item evaluation by considering the entire list of items as a collective entity. The goal is to optimize the ordering of this list to best match the user’s preferences, ranking items from most to least relevant. 

Data for GPT recommender

 

data_1.png                      data_2.png

 

Prompt design for 3 types of GPT Recommender 

  • Pointwise 
  • Pairwise 
  • Listwise 

pointwise recommender.png 

pairwise recommender.png

 

listwise recommender.png

 

Evaluation for 3 types of GPT Recommender 

Evaluating three types of GPT Recommender systems—Pointwise, Pairwise, and Listwise—plays a crucial role in optimizing the performance and relevance of recommendations in various contexts. Each approach has its unique methodology for evaluating the ranking and recommending items. 

 

## pointwise recommender metrics: regression metrics  

MSE, MAE  

regression metrics.png

 

## pair wise recommender metrics: classification metrics  

Precision, Recall

Precision.png

 

Recall.png

 

## list wise recommender metrics: Ranking metrics  

NDCG 

NDCG.png

 

Summarize product features   

Use ChatGPT to summarize the main features of a product into a short, easy-to-read summary of 1-2 paragraphs. It involves looking through a lot of detailed information on several pages and boiling it down to just the most important points, giving you a quick and clear picture of what the product is all about.

 

Data for Product feature summarization  
 
str_product_specs = """ 

Front view of Surface Laptop 5 in Sage with a sage green blossom on the Windows 11 start screen. 

Sleek, thin, light 

13.5” PixelSense™ touchscreen for ultra-portable productivity, or larger 15” for split-screen multitasking. 

 

Sleek and super-light weight laptop starting at 2.80 lbs (1,272 g) with an exceptionally comfortable keyboard. 

 

Warm, sophisticated Alcantara® or edgy, cool metal, and bold colors including new Sage.Footnote1 

Front view of Surface Laptop 5 in platinum with a green blossom on the Windows 11 start screen. 

Blazing fast 

Snappy multitasking with powerful 12th Gen Intel® Core™ i5/i7 processors built on the Intel® Evo™ platform. 

 

Lightning-fast Thunderbolt™ 4 connects a 4K monitor, charges your laptop, and delivers faster data transfer for large video files. 

 

Reliable all-day battery.Footnote2 

Surface Laptop 5 shown from the back with the lid slightly closed. 

Elevated experiences 

Look and sound your best on calls with Studio Mics and enhanced camera experiences, powered by Windows 11. 

 

Cinematic entertainment. Ultra-vivid colors with Dolby Vision IQ™3 and sound that moves all around you with Dolby Atmos®.Footnote4 

Side view of Laptop 5 with the screen closed. 

Built-in security for work and play 

Peace of mind from the moment you sign in, with Windows Hello and built-in Windows 11 security. 

 

Get productive and jump start your creative ideas with Microsoft 365 and video editing with ClipChamp. 

 

Secured OneDrive cloud storage for your Microsoft 365 files. 

 

Play together on Windows PCs with Xbox Game Pass Ultimate.Footnote7 

Tech specs 

Processor 

Surface Laptop 5 13.5”: 

12th Gen Intel® Core™ i5-1235U processor 

12th Gen Intel® Core™ i7-1255U processor 

Built on the Intel® Evo™ platform 

 

Surface Laptop 5 15”: 

12th Gen Intel® Core™ i7-1255U processor 

Built on the Intel® Evo™ platform 

Graphics 

Intel® Iris® Xe Graphics 

Memory and StorageFootnote8 

Surface Laptop 5 13.5” 

8GB, 16GB LPDDR5x RAM 

RemovableFootnote9 solid-state drive (SSD) options: 256GB, 512GB 

 

Surface Laptop 5 15” 

8GB, 16GB, or 32GB LPDDR5x RAM 

RemovableFootnote9 solid-state drive (SSD) options: 256GB, 512GB, or 1TB 

Display 

Surface Laptop 5 13.5”: 

Screen: 13.5” PixelSense™ Display 

Resolution: 2256 x 1504 (201 PPI) 

Aspect ratio: 3:2 

Contrast ratio 1300:1 

Color profile: sRGB, and Vivid 

Individually color-calibrated display 

Dolby Vision IQ™Footnote3 support 

Touch: 10-point multi-touch 

Gorilla® Glass 3 display on laptop with Alcantara® palm rest 

Gorilla® Glass 5 display on laptop with metal palm rest 

 

Surface Laptop 5 15”: 

Screen: 15” PixelSense™ Display 

Resolution: 2496 x 1664 (201 PPI) 

Aspect ratio: 3:2 

Contrast ratio 1300:1 

Color profile: sRGB, and Vivid 

Individually color-calibrated display 

Dolby Vision IQ™Footnote3. support 

Touch: 10-point multi-touch 

Gorilla® Glass 5 

BatteryFootnote2 

Surface Laptop 5 13.5”: 

Up to 18 hours of typical device usage 

 

Surface Laptop 5 15”: 

Up to 17 hours of typical device usage 

Size and Weight 

Surface Laptop 5 13.5”: 

Length: 12.1” (308 mm) 

Width: 8.8” (223 mm) 

Height: .57” (14.5 mm) 

Weight: Fabric: 2.80 lbs (1,272 g) 

Metal: 2.86 lbs (1,297 g) 

 

Surface Laptop 5 15”: 

Length: 13.4” (340 mm) 

Width: 9.6” (244 mm) 

Height: .58” (14.7 mm) 

Weight: 3.44 lbs (1,545 g) 

Security 

Firmware TPM 2.0 is a security processor that is designed to give you peace of mind. 

Windows Hello face sign-in 

Video/Cameras 

Windows Hello Face Authentication camera 

720p HD front facing camera 

Audio 

Omnisonic® Speakers with Dolby® Atmos™Footnote4 

Mics 

Dual far-field Studio microphones 

Connections 

1 x USB-C® with USB 4.0/Thunderbolt™ 4 

1 x USB-A 3.1 

3.5mm headphone jack 

1 x Surface Connect port 

Network and connectivity 

Wi-Fi 6: 802.11ax compatible 

Bluetooth® Wireless 5.1 technology 

Pen and accessories compatibility 

Designed for Surface Pen* 

Compatible with Microsoft Pen Protocol (MPP) 

Software 

 

Windows 11 Home 

Preloaded Microsoft 365 Apps5 

Microsoft 365 Family 30-day trial6 

Xbox Game Pass Ultimate 30-day trialFootnote7 

Accessibility 

Compatible with Surface Adaptive Kit 

Compatible with Microsoft Adaptive Accessories 

Include Windows Accessibility Feature – Learn More Accessibility Features | Microsoft Accessibility 

Discover more Microsoft Accessible Devices & Products - Accessible Devices & Products for PC & Gaming | Assistive Tech Accessories - Microsoft Store 

SustainabilityFootnote12 

Meets ENERGY STAR® requirements 

Registered EPEAT® Gold in the US and Canada11 

Sustainable Products & Solutions | Microsoft CSR 

Exterior 

Casing: Aluminum 

Power and Volume buttons on keyboard 

 

Surface Laptop 5 13.5” colors: 

Platinum with Alcantara® material palm rest 

Matte Black with metal palm rest 

Sage with metal palm rest 

Sandstone with metal palm rest 

 

Surface Laptop 5 15” colors:1 

Platinum with metal palm rest 

Matte Black with metal palm rest 

Sensors 

Ambient light sensor 

What’s in the box 

Surface Laptop 5 13.5” and 15”: 

Power Supply 

Quick Start Guide 

Safety and warranty documents 

Keyboard Compatibility 

Activation: Moving keys 

Backlight 

Layout: English, full row of function keys (F1 – F12) 

Windows key and dedicated buttons for media controls, screen brightness 

WarrantyFootnote10 

1-year limited hardware warranty 

""" 

 

Prompt Design for summarizing product features 

response_sample = openai.ChatCompletion.create( 

    engine='gpt-35-turbo-0613', # The deployment name you chose when you deployed the GPT-35-Turbo or GPT-4 model. 

    messages=[ 

        {"role": "system", "content": "Assistant summarize product features."}, 

        {"role": "user", "content": f""" 

        Here are the full product specs: {str_product_specs} 

        Based on this history, please summary the product features highlight into a few sentences. 

        """}, 

            ] 

        ) 

 

str_chatgpt_summary = response_sample['choices'][0]['message']['content'] 

print(str_chatgpt_summary) 

 

 

Evaluation for product summarization 
Regarding product summarization, we adopt a comprehensive scoring system encompassing n-gram Bilingual Evaluation Understudy (BLEU-n), n-gram Recall-Oriented Understudy for Gisting Evaluation (ROUGE-n), and Large Language Models (LLM) evaluation. 

 

from nltk.translate.bleu_score import sentence_bleu 

 

 

hypothesis  = str_chatgpt_summary 

reference_summary = """Front view of Surface Laptop 5 in Sage with a sage green blossom on the Windows 11 start screen. 

Sleek, thin, light 

13.5” PixelSense™ touchscreen for ultra-portable productivity, or larger 15” for split-screen multitasking. 

 

Sleek and super-light weight laptop starting at 2.80 lbs (1,272 g) with an exceptionally comfortable keyboard. 

 

Warm, sophisticated Alcantara® or edgy, cool metal, and bold colors including new Sage.Footnote1 

Front view of Surface Laptop 5 in platinum with a green blossom on the Windows 11 start screen. 

Blazing fast 

Snappy multitasking with powerful 12th Gen Intel® Core™ i5/i7 processors built on the Intel® Evo™ platform. 

 

Lightning-fast Thunderbolt™ 4 connects a 4K monitor, charges your laptop, and delivers faster data transfer for large video files. 

 

Reliable all-day battery.Footnote2 

Surface Laptop 5 shown from the back with the lid slightly closed. 

Elevated experiences 

Look and sound your best on calls with Studio Mics and enhanced camera experiences, powered by Windows 11. 

 

Cinematic entertainment. Ultra-vivid colors with Dolby Vision IQ™3 and sound that moves all around you with Dolby Atmos®.Footnote4 

Side view of Laptop 5 with the screen closed. 

Built-in security for work and play 

Peace of mind from the moment you sign in, with Windows Hello and built-in Windows 11 security. 

 

Get productive and jump start your creative ideas with Microsoft 365 and video editing with ClipChamp. 

 

Secured OneDrive cloud storage for your Microsoft 365 files. 

 

Play together on Windows PCs with Xbox Game Pass Ultimate.Footnote7""" 

Evaluation metrics.png

 

Product review generation 

Use GPT to create product reviews by compiling and synthesizing insights from numerous user reviews.

Data for Product review

Data for Product review.jpg

 

Data preprocessing.png

 

Prompt design for product review generation 

Here, a straightforward system and user prompt are demonstrated to guide GPT in creating an overarching review from several individual user reviews.

 

response_sample = openai.ChatCompletion.create( 

    engine='gpt-35-turbo-0613', # The deployment name you chose when you deployed the GPT-35-Turbo or GPT-4 model. 

    messages=[ 

        {"role": "system", "content": "Assistant summarize product reviews."}, 

        {"role": "user", "content": f""" 

        Here are the product reviews from several users: {str_concatenated_reviews_sample} 

        Based on this history, please summary the product reviews into 1 to 2 sentences. 

        """}, 

            ] 

        ) 

 

str_chatgpt_summary = response_sample['choices'][0]['message']['content'] 

print(str_chatgpt_summary) 

 

Evaluation for product review generation:

BLEU score.png

 

Rouge score.png

 


Engineering Implementation 

Engineering Execution is beyond the scope of this work. Some implementation ideas are listed here in case a full product is intended to be built. 

  1. Utilization of Semantic Kernel: Our system is designed to seamlessly navigate between three distinct branches, ensuring smooth operations and effective data management. 
  1. Integration with Database: We leverage the robust and scalable Azure storage for our data management needs, facilitating efficient data handling and retrieval. 

Conclusion 

In conclusion, the ChatGPT Ecommerce System offers an innovative solution that surpasses the limitations of traditional recommendation methods. It specifically caters to scenarios with limited task-specific data, offering robust solutions for cold start problems associated with new users. Key features include recommendation based on user-item interaction history, product reviews generation, and product feature summarization for email campaigns. It also addresses inherent biases, including Position Bias and Popularity Bias, which can impact Large Language Models' ranking. Various data sources and evaluation metrics are leveraged to ensure a comprehensive and accurate ecommerce recommendation system. All in all, this system presents an advanced, user-centric approach to ecommerce, enhancing user experience and efficiency in the rapidly evolving digital marketplace. 

 

References 

 

Co-Authors
Version history
Last update:
‎Apr 08 2024 10:10 AM
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