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.
","body":"ChatGPT Ecommerce System
\nThis 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.
\n
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.
\n\n
Features
\nUse cases cover three areas:
\n1. Recommendation based on user-item interaction history.
\nPointwise 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.
\n3 Product review generation. Generation high level product reviews from lot of users' reviews.
\n\n
System Architecture
\nThis 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.
\n\n
\n
\n
Biases on LLM Ranking and How to Address Them
\nPosition Bias
\nOne 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.
\n\n
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.
\n\n
Popularity Bias
\nAnother 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.
\nReference: [2305.08845] Large Language Models are Zero-Shot Rankers for Recommender Systems (arxiv.org)
\n\n
3 types of GPT Recommender
\nData for GPT recommender
\n\n
\n
\n
Prompt design for 3 types of GPT Recommender
\n\n
\n
\n
\n
\n
Evaluation for 3 types of GPT Recommender
\nEvaluating 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.
\n\n
## pointwise recommender metrics: regression metrics
\nMSE, MAE
\n\n
\n
## pair wise recommender metrics: classification metrics
\nPrecision, Recall
\n\n
\n
\n
\n
## list wise recommender metrics: Ranking metrics
\nNDCG
\n\n
\n
Summarize product features
\nUse 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.
\n\n
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.
\nSleek, thin, light
\n13.5” PixelSense™ touchscreen for ultra-portable productivity, or larger 15” for split-screen multitasking.
\n\n
Sleek and super-light weight laptop starting at 2.80 lbs (1,272 g) with an exceptionally comfortable keyboard.
\n\n
Warm, sophisticated Alcantara® or edgy, cool metal, and bold colors including new Sage.Footnote1
\nFront view of Surface Laptop 5 in platinum with a green blossom on the Windows 11 start screen.
\nBlazing fast
\nSnappy multitasking with powerful 12th Gen Intel® Core™ i5/i7 processors built on the Intel® Evo™ platform.
\n\n
Lightning-fast Thunderbolt™ 4 connects a 4K monitor, charges your laptop, and delivers faster data transfer for large video files.
\n\n
Reliable all-day battery.Footnote2
\nSurface Laptop 5 shown from the back with the lid slightly closed.
\nElevated experiences
\nLook and sound your best on calls with Studio Mics and enhanced camera experiences, powered by Windows 11.
\n\n
Cinematic entertainment. Ultra-vivid colors with Dolby Vision IQ™3 and sound that moves all around you with Dolby Atmos®.Footnote4
\nSide view of Laptop 5 with the screen closed.
\nBuilt-in security for work and play
\nPeace of mind from the moment you sign in, with Windows Hello and built-in Windows 11 security.
\n\n
Get productive and jump start your creative ideas with Microsoft 365 and video editing with ClipChamp.
\n\n
Secured OneDrive cloud storage for your Microsoft 365 files.
\n\n
Play together on Windows PCs with Xbox Game Pass Ultimate.Footnote7
\nTech specs
\nProcessor
\nSurface Laptop 5 13.5”:
\n12th Gen Intel® Core™ i5-1235U processor
\n12th Gen Intel® Core™ i7-1255U processor
\nBuilt on the Intel® Evo™ platform
\n\n
Surface Laptop 5 15”:
\n12th Gen Intel® Core™ i7-1255U processor
\nBuilt on the Intel® Evo™ platform
\nGraphics
\nIntel® Iris® Xe Graphics
\nMemory and StorageFootnote8
\nSurface Laptop 5 13.5”
\n8GB, 16GB LPDDR5x RAM
\nRemovableFootnote9 solid-state drive (SSD) options: 256GB, 512GB
\n\n
Surface Laptop 5 15”
\n8GB, 16GB, or 32GB LPDDR5x RAM
\nRemovableFootnote9 solid-state drive (SSD) options: 256GB, 512GB, or 1TB
\nDisplay
\nSurface Laptop 5 13.5”:
\nScreen: 13.5” PixelSense™ Display
\nResolution: 2256 x 1504 (201 PPI)
\nAspect ratio: 3:2
\nContrast ratio 1300:1
\nColor profile: sRGB, and Vivid
\nIndividually color-calibrated display
\nDolby Vision IQ™Footnote3 support
\nTouch: 10-point multi-touch
\nGorilla® Glass 3 display on laptop with Alcantara® palm rest
\nGorilla® Glass 5 display on laptop with metal palm rest
\n\n
Surface Laptop 5 15”:
\nScreen: 15” PixelSense™ Display
\nResolution: 2496 x 1664 (201 PPI)
\nAspect ratio: 3:2
\nContrast ratio 1300:1
\nColor profile: sRGB, and Vivid
\nIndividually color-calibrated display
\nDolby Vision IQ™Footnote3. support
\nTouch: 10-point multi-touch
\nGorilla® Glass 5
\nBatteryFootnote2
\nSurface Laptop 5 13.5”:
\nUp to 18 hours of typical device usage
\n\n
Surface Laptop 5 15”:
\nUp to 17 hours of typical device usage
\nSize and Weight
\nSurface Laptop 5 13.5”:
\nLength: 12.1” (308 mm)
\nWidth: 8.8” (223 mm)
\nHeight: .57” (14.5 mm)
\nWeight: Fabric: 2.80 lbs (1,272 g)
\nMetal: 2.86 lbs (1,297 g)
\n\n
Surface Laptop 5 15”:
\nLength: 13.4” (340 mm)
\nWidth: 9.6” (244 mm)
\nHeight: .58” (14.7 mm)
\nWeight: 3.44 lbs (1,545 g)
\nSecurity
\nFirmware TPM 2.0 is a security processor that is designed to give you peace of mind.
\nWindows Hello face sign-in
\nVideo/Cameras
\nWindows Hello Face Authentication camera
\n720p HD front facing camera
\nAudio
\nOmnisonic® Speakers with Dolby® Atmos™Footnote4
\nMics
\nDual far-field Studio microphones
\nConnections
\n1 x USB-C® with USB 4.0/Thunderbolt™ 4
\n1 x USB-A 3.1
\n3.5mm headphone jack
\n1 x Surface Connect port
\nNetwork and connectivity
\nWi-Fi 6: 802.11ax compatible
\nBluetooth® Wireless 5.1 technology
\nPen and accessories compatibility
\nDesigned for Surface Pen*
\nCompatible with Microsoft Pen Protocol (MPP)
\nSoftware
\n\n
Windows 11 Home
\nPreloaded Microsoft 365 Apps5
\nMicrosoft 365 Family 30-day trial6
\nXbox Game Pass Ultimate 30-day trialFootnote7
\nAccessibility
\nCompatible with Surface Adaptive Kit
\nCompatible with Microsoft Adaptive Accessories
\nInclude Windows Accessibility Feature – Learn More Accessibility Features | Microsoft Accessibility
\nDiscover more Microsoft Accessible Devices & Products - Accessible Devices & Products for PC & Gaming | Assistive Tech Accessories - Microsoft Store
\nSustainabilityFootnote12
\nMeets ENERGY STAR® requirements
\nRegistered EPEAT® Gold in the US and Canada11
\nSustainable Products & Solutions | Microsoft CSR
\nExterior
\nCasing: Aluminum
\nPower and Volume buttons on keyboard
\n\n
Surface Laptop 5 13.5” colors:
\nPlatinum with Alcantara® material palm rest
\nMatte Black with metal palm rest
\nSage with metal palm rest
\nSandstone with metal palm rest
\n\n
Surface Laptop 5 15” colors:1
\nPlatinum with metal palm rest
\nMatte Black with metal palm rest
\nSensors
\nAmbient light sensor
\nWhat’s in the box
\nSurface Laptop 5 13.5” and 15”:
\nPower Supply
\nQuick Start Guide
\nSafety and warranty documents
\nKeyboard Compatibility
\nActivation: Moving keys
\nBacklight
\nLayout: English, full row of function keys (F1 – F12)
\nWindows key and dedicated buttons for media controls, screen brightness
\nWarrantyFootnote10
\n1-year limited hardware warranty
\n\"\"\"
\n\n
Prompt Design for summarizing product features
\nresponse_sample = openai.ChatCompletion.create(
\nengine='gpt-35-turbo-0613', # The deployment name you chose when you deployed the GPT-35-Turbo or GPT-4 model.
\nmessages=[
\n{\"role\": \"system\", \"content\": \"Assistant summarize product features.\"},
\n{\"role\": \"user\", \"content\": f\"\"\"
\nHere are the full product specs: {str_product_specs}
\nBased on this history, please summary the product features highlight into a few sentences.
\n\"\"\"},
\n]
\n)
\n\n
str_chatgpt_summary = response_sample['choices'][0]['message']['content']
\nprint(str_chatgpt_summary)
\n\n
\n
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.
\n
from nltk.translate.bleu_score import sentence_bleu
\n\n
\n
hypothesis = str_chatgpt_summary
\nreference_summary = \"\"\"Front view of Surface Laptop 5 in Sage with a sage green blossom on the Windows 11 start screen.
\nSleek, thin, light
\n13.5” PixelSense™ touchscreen for ultra-portable productivity, or larger 15” for split-screen multitasking.
\n\n
Sleek and super-light weight laptop starting at 2.80 lbs (1,272 g) with an exceptionally comfortable keyboard.
\n\n
Warm, sophisticated Alcantara® or edgy, cool metal, and bold colors including new Sage.Footnote1
\nFront view of Surface Laptop 5 in platinum with a green blossom on the Windows 11 start screen.
\nBlazing fast
\nSnappy multitasking with powerful 12th Gen Intel® Core™ i5/i7 processors built on the Intel® Evo™ platform.
\n\n
Lightning-fast Thunderbolt™ 4 connects a 4K monitor, charges your laptop, and delivers faster data transfer for large video files.
\n\n
Reliable all-day battery.Footnote2
\nSurface Laptop 5 shown from the back with the lid slightly closed.
\nElevated experiences
\nLook and sound your best on calls with Studio Mics and enhanced camera experiences, powered by Windows 11.
\n\n
Cinematic entertainment. Ultra-vivid colors with Dolby Vision IQ™3 and sound that moves all around you with Dolby Atmos®.Footnote4
\nSide view of Laptop 5 with the screen closed.
\nBuilt-in security for work and play
\nPeace of mind from the moment you sign in, with Windows Hello and built-in Windows 11 security.
\n\n
Get productive and jump start your creative ideas with Microsoft 365 and video editing with ClipChamp.
\n\n
Secured OneDrive cloud storage for your Microsoft 365 files.
\n\n
Play together on Windows PCs with Xbox Game Pass Ultimate.Footnote7\"\"\"
\n\n
\n
Product review generation
\nUse GPT to create product reviews by compiling and synthesizing insights from numerous user reviews.
\nData for Product review
\n\n
\n
\n
\n
Prompt design for product review generation
\nHere, a straightforward system and user prompt are demonstrated to guide GPT in creating an overarching review from several individual user reviews.
\n\n
response_sample = openai.ChatCompletion.create(
\nengine='gpt-35-turbo-0613', # The deployment name you chose when you deployed the GPT-35-Turbo or GPT-4 model.
\nmessages=[
\n{\"role\": \"system\", \"content\": \"Assistant summarize product reviews.\"},
\n{\"role\": \"user\", \"content\": f\"\"\"
\nHere are the product reviews from several users: {str_concatenated_reviews_sample}
\nBased on this history, please summary the product reviews into 1 to 2 sentences.
\n\"\"\"},
\n]
\n)
\n\n
str_chatgpt_summary = response_sample['choices'][0]['message']['content']
\nprint(str_chatgpt_summary)
\n\n
Evaluation for product review generation:
\n\n
\n
\n
\n
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.
\nConclusion
\nIn 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.
\n\n
References
\n","body@stringLength":"125803","rawBody":"
ChatGPT Ecommerce System
\nThis 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.
\n
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.
\n\n
Features
\nUse cases cover three areas:
\n1. Recommendation based on user-item interaction history.
\nPointwise 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.
\n3 Product review generation. Generation high level product reviews from lot of users' reviews.
\n\n
System Architecture
\nThis 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.
\n\n
\n
\n
Biases on LLM Ranking and How to Address Them
\nPosition Bias
\nOne 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.
\n\n
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.
\n\n
Popularity Bias
\nAnother 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.
\nReference: [2305.08845] Large Language Models are Zero-Shot Rankers for Recommender Systems (arxiv.org)
\n\n
3 types of GPT Recommender
\nData for GPT recommender
\n\n
\n
Prompt design for 3 types of GPT Recommender
\n\n
\n
Evaluation for 3 types of GPT Recommender
\nEvaluating 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.
\n\n
## pointwise recommender metrics: regression metrics
\nMSE, MAE
\n\n
## pair wise recommender metrics: classification metrics
\nPrecision, Recall
\n\n
\n
## list wise recommender metrics: Ranking metrics
\nNDCG
\n\n
Summarize product features
\nUse 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.
\n\n
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.
\nSleek, thin, light
\n13.5” PixelSense™ touchscreen for ultra-portable productivity, or larger 15” for split-screen multitasking.
\n\n
Sleek and super-light weight laptop starting at 2.80 lbs (1,272 g) with an exceptionally comfortable keyboard.
\n\n
Warm, sophisticated Alcantara® or edgy, cool metal, and bold colors including new Sage.Footnote1
\nFront view of Surface Laptop 5 in platinum with a green blossom on the Windows 11 start screen.
\nBlazing fast
\nSnappy multitasking with powerful 12th Gen Intel® Core™ i5/i7 processors built on the Intel® Evo™ platform.
\n\n
Lightning-fast Thunderbolt™ 4 connects a 4K monitor, charges your laptop, and delivers faster data transfer for large video files.
\n\n
Reliable all-day battery.Footnote2
\nSurface Laptop 5 shown from the back with the lid slightly closed.
\nElevated experiences
\nLook and sound your best on calls with Studio Mics and enhanced camera experiences, powered by Windows 11.
\n\n
Cinematic entertainment. Ultra-vivid colors with Dolby Vision IQ™3 and sound that moves all around you with Dolby Atmos®.Footnote4
\nSide view of Laptop 5 with the screen closed.
\nBuilt-in security for work and play
\nPeace of mind from the moment you sign in, with Windows Hello and built-in Windows 11 security.
\n\n
Get productive and jump start your creative ideas with Microsoft 365 and video editing with ClipChamp.
\n\n
Secured OneDrive cloud storage for your Microsoft 365 files.
\n\n
Play together on Windows PCs with Xbox Game Pass Ultimate.Footnote7
\nTech specs
\nProcessor
\nSurface Laptop 5 13.5”:
\n12th Gen Intel® Core™ i5-1235U processor
\n12th Gen Intel® Core™ i7-1255U processor
\nBuilt on the Intel® Evo™ platform
\n\n
Surface Laptop 5 15”:
\n12th Gen Intel® Core™ i7-1255U processor
\nBuilt on the Intel® Evo™ platform
\nGraphics
\nIntel® Iris® Xe Graphics
\nMemory and StorageFootnote8
\nSurface Laptop 5 13.5”
\n8GB, 16GB LPDDR5x RAM
\nRemovableFootnote9 solid-state drive (SSD) options: 256GB, 512GB
\n\n
Surface Laptop 5 15”
\n8GB, 16GB, or 32GB LPDDR5x RAM
\nRemovableFootnote9 solid-state drive (SSD) options: 256GB, 512GB, or 1TB
\nDisplay
\nSurface Laptop 5 13.5”:
\nScreen: 13.5” PixelSense™ Display
\nResolution: 2256 x 1504 (201 PPI)
\nAspect ratio: 3:2
\nContrast ratio 1300:1
\nColor profile: sRGB, and Vivid
\nIndividually color-calibrated display
\nDolby Vision IQ™Footnote3 support
\nTouch: 10-point multi-touch
\nGorilla® Glass 3 display on laptop with Alcantara® palm rest
\nGorilla® Glass 5 display on laptop with metal palm rest
\n\n
Surface Laptop 5 15”:
\nScreen: 15” PixelSense™ Display
\nResolution: 2496 x 1664 (201 PPI)
\nAspect ratio: 3:2
\nContrast ratio 1300:1
\nColor profile: sRGB, and Vivid
\nIndividually color-calibrated display
\nDolby Vision IQ™Footnote3. support
\nTouch: 10-point multi-touch
\nGorilla® Glass 5
\nBatteryFootnote2
\nSurface Laptop 5 13.5”:
\nUp to 18 hours of typical device usage
\n\n
Surface Laptop 5 15”:
\nUp to 17 hours of typical device usage
\nSize and Weight
\nSurface Laptop 5 13.5”:
\nLength: 12.1” (308 mm)
\nWidth: 8.8” (223 mm)
\nHeight: .57” (14.5 mm)
\nWeight: Fabric: 2.80 lbs (1,272 g)
\nMetal: 2.86 lbs (1,297 g)
\n\n
Surface Laptop 5 15”:
\nLength: 13.4” (340 mm)
\nWidth: 9.6” (244 mm)
\nHeight: .58” (14.7 mm)
\nWeight: 3.44 lbs (1,545 g)
\nSecurity
\nFirmware TPM 2.0 is a security processor that is designed to give you peace of mind.
\nWindows Hello face sign-in
\nVideo/Cameras
\nWindows Hello Face Authentication camera
\n720p HD front facing camera
\nAudio
\nOmnisonic® Speakers with Dolby® Atmos™Footnote4
\nMics
\nDual far-field Studio microphones
\nConnections
\n1 x USB-C® with USB 4.0/Thunderbolt™ 4
\n1 x USB-A 3.1
\n3.5mm headphone jack
\n1 x Surface Connect port
\nNetwork and connectivity
\nWi-Fi 6: 802.11ax compatible
\nBluetooth® Wireless 5.1 technology
\nPen and accessories compatibility
\nDesigned for Surface Pen*
\nCompatible with Microsoft Pen Protocol (MPP)
\nSoftware
\n\n
Windows 11 Home
\nPreloaded Microsoft 365 Apps5
\nMicrosoft 365 Family 30-day trial6
\nXbox Game Pass Ultimate 30-day trialFootnote7
\nAccessibility
\nCompatible with Surface Adaptive Kit
\nCompatible with Microsoft Adaptive Accessories
\nInclude Windows Accessibility Feature – Learn More Accessibility Features | Microsoft Accessibility
\nDiscover more Microsoft Accessible Devices & Products - Accessible Devices & Products for PC & Gaming | Assistive Tech Accessories - Microsoft Store
\nSustainabilityFootnote12
\nMeets ENERGY STAR® requirements
\nRegistered EPEAT® Gold in the US and Canada11
\nSustainable Products & Solutions | Microsoft CSR
\nExterior
\nCasing: Aluminum
\nPower and Volume buttons on keyboard
\n\n
Surface Laptop 5 13.5” colors:
\nPlatinum with Alcantara® material palm rest
\nMatte Black with metal palm rest
\nSage with metal palm rest
\nSandstone with metal palm rest
\n\n
Surface Laptop 5 15” colors:1
\nPlatinum with metal palm rest
\nMatte Black with metal palm rest
\nSensors
\nAmbient light sensor
\nWhat’s in the box
\nSurface Laptop 5 13.5” and 15”:
\nPower Supply
\nQuick Start Guide
\nSafety and warranty documents
\nKeyboard Compatibility
\nActivation: Moving keys
\nBacklight
\nLayout: English, full row of function keys (F1 – F12)
\nWindows key and dedicated buttons for media controls, screen brightness
\nWarrantyFootnote10
\n1-year limited hardware warranty
\n\"\"\"
\n\n
Prompt Design for summarizing product features
\nresponse_sample = openai.ChatCompletion.create(
\nengine='gpt-35-turbo-0613', # The deployment name you chose when you deployed the GPT-35-Turbo or GPT-4 model.
\nmessages=[
\n{\"role\": \"system\", \"content\": \"Assistant summarize product features.\"},
\n{\"role\": \"user\", \"content\": f\"\"\"
\nHere are the full product specs: {str_product_specs}
\nBased on this history, please summary the product features highlight into a few sentences.
\n\"\"\"},
\n]
\n)
\n\n
str_chatgpt_summary = response_sample['choices'][0]['message']['content']
\nprint(str_chatgpt_summary)
\n\n
\n
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.
\n
from nltk.translate.bleu_score import sentence_bleu
\n\n
\n
hypothesis = str_chatgpt_summary
\nreference_summary = \"\"\"Front view of Surface Laptop 5 in Sage with a sage green blossom on the Windows 11 start screen.
\nSleek, thin, light
\n13.5” PixelSense™ touchscreen for ultra-portable productivity, or larger 15” for split-screen multitasking.
\n\n
Sleek and super-light weight laptop starting at 2.80 lbs (1,272 g) with an exceptionally comfortable keyboard.
\n\n
Warm, sophisticated Alcantara® or edgy, cool metal, and bold colors including new Sage.Footnote1
\nFront view of Surface Laptop 5 in platinum with a green blossom on the Windows 11 start screen.
\nBlazing fast
\nSnappy multitasking with powerful 12th Gen Intel® Core™ i5/i7 processors built on the Intel® Evo™ platform.
\n\n
Lightning-fast Thunderbolt™ 4 connects a 4K monitor, charges your laptop, and delivers faster data transfer for large video files.
\n\n
Reliable all-day battery.Footnote2
\nSurface Laptop 5 shown from the back with the lid slightly closed.
\nElevated experiences
\nLook and sound your best on calls with Studio Mics and enhanced camera experiences, powered by Windows 11.
\n\n
Cinematic entertainment. Ultra-vivid colors with Dolby Vision IQ™3 and sound that moves all around you with Dolby Atmos®.Footnote4
\nSide view of Laptop 5 with the screen closed.
\nBuilt-in security for work and play
\nPeace of mind from the moment you sign in, with Windows Hello and built-in Windows 11 security.
\n\n
Get productive and jump start your creative ideas with Microsoft 365 and video editing with ClipChamp.
\n\n
Secured OneDrive cloud storage for your Microsoft 365 files.
\n\n
Play together on Windows PCs with Xbox Game Pass Ultimate.Footnote7\"\"\"
\n\n
Product review generation
\nUse GPT to create product reviews by compiling and synthesizing insights from numerous user reviews.
\nData for Product review
\n\n
\n
Prompt design for product review generation
\nHere, a straightforward system and user prompt are demonstrated to guide GPT in creating an overarching review from several individual user reviews.
\n\n
response_sample = openai.ChatCompletion.create(
\nengine='gpt-35-turbo-0613', # The deployment name you chose when you deployed the GPT-35-Turbo or GPT-4 model.
\nmessages=[
\n{\"role\": \"system\", \"content\": \"Assistant summarize product reviews.\"},
\n{\"role\": \"user\", \"content\": f\"\"\"
\nHere are the product reviews from several users: {str_concatenated_reviews_sample}
\nBased on this history, please summary the product reviews into 1 to 2 sentences.
\n\"\"\"},
\n]
\n)
\n\n
str_chatgpt_summary = response_sample['choices'][0]['message']['content']
\nprint(str_chatgpt_summary)
\n\n
Evaluation for product review generation:
\n\n
\n
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.
\nConclusion
\nIn 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.
\n\n
References
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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.
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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 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