API Endpoints For Reviewer & Product History

Nov 3, 2025 by Admin 45 views

Hey guys!

So, we've got Sprint Task 3.2 lined up, and it's all about building some super helpful API endpoints. These endpoints are designed to give our Amazon integrity analysts a complete picture of what's going on with flagged reviews. We're talking about diving deep into reviewer history, product history, related reviews, seller info, and even purchase patterns. This way, they can make really informed decisions. Let's break down what we need to do.

User Story Context

Before we jump into the nitty-gritty, let's remember the User Story 3: As an Amazon integrity analyst, I want to access the full context of a flagged review (reviewer history, product history, related reviews/sellers, purchase patterns), so I can make an informed decision.

This user story is our North Star. Everything we build in this task should directly support this goal. Our analysts need comprehensive data at their fingertips to spot and address any integrity issues effectively. The estimate for this task is 8 points, so let's make every point count!

Breaking Down Task 3.2: API Endpoints

Task 3.2 is all about developing API endpoints to retrieve reviewer history, product history, and related reviews/purchase patterns. Let's dive into each of these components to understand what needs to be built.

1. Reviewer History Endpoint

The reviewer history endpoint is crucial for understanding the behavior and credibility of a reviewer. When an analyst flags a review, they need to quickly assess whether the reviewer has a pattern of suspicious activity.

Key Data Points to Include:

Reviews Written: A list of all reviews written by the reviewer, including the product, rating, and date of the review.
Rating Distribution: A summary of the ratings given by the reviewer (e.g., number of 5-star, 4-star, etc.). This can help identify if the reviewer predominantly gives positive or negative reviews.
Review Content Analysis: A text analysis of the reviews to identify common themes, keywords, or phrases. This can help detect if the reviewer is using canned responses or following a specific script.
Helpfulness Votes: Data on how many helpfulness votes the reviewer's reviews have received. This can indicate the quality and relevance of their reviews.
Reporting History: Any past reports or flags associated with the reviewer, including the reasons for the flags and the outcomes.
Verified Purchase Status: Whether the reviewer consistently makes verified purchases before writing reviews.
Review Velocity: The frequency at which the reviewer posts reviews. A sudden spike in reviews could be a red flag.
Network Analysis: Identify other reviewers or accounts that frequently interact with this reviewer, which may uncover coordinated behavior.

Technical Considerations:

Endpoint Design: The endpoint should accept a reviewer ID as a parameter and return a JSON object containing all the relevant data points.
Performance: The endpoint should be optimized for fast retrieval, as analysts need to quickly access this information.
Data Sources: The data will likely come from multiple sources, including review databases, user databases, and reporting systems. We need to integrate these sources seamlessly.

2. Product History Endpoint

The product history endpoint provides insights into the performance and reputation of a product. This helps analysts understand if a flagged review is part of a larger pattern of issues with the product.

Key Data Points to Include:

Sales Data: Historical sales data, including sales volume, revenue, and sales trends.
Review Trends: Trends in review scores over time, including average rating, number of reviews, and sentiment analysis.
Review Content Analysis: Analysis of review content to identify common positive and negative themes.
Competitive Analysis: Comparison of the product's reviews and ratings against similar products.
Reporting History: Any past reports or flags associated with the product, including the reasons for the flags and the outcomes.
Seller Information: Details about the seller, including their history, reputation, and other products they sell.
Price History: Historical pricing data to identify any unusual price fluctuations.
Inventory Levels: Data on inventory levels to detect any stock issues that might affect reviews.

Technical Considerations:

Endpoint Design: The endpoint should accept a product ID (e.g., ASIN) as a parameter and return a JSON object containing the relevant data points.
Data Aggregation: This endpoint will likely require aggregating data from multiple sources, including sales databases, review databases, and seller databases.
Real-time Updates: Some data points, such as review trends, should be updated in real-time to provide the most current information.

3. Related Reviews and Purchase Patterns Endpoint

This endpoint focuses on identifying connections between reviews and purchase behaviors. It helps analysts uncover coordinated review campaigns or other manipulative tactics.

Key Data Points to Include:

Co-Reviewers: Identification of other reviewers who frequently review the same products as the reviewer in question.
Purchase Patterns: Analysis of purchase patterns to identify suspicious behaviors, such as bulk purchases or purchases followed immediately by reviews.
Reviewer Overlap: Identification of reviewers who have reviewed the same products within a short period.
Seller Relationships: Information on the relationships between reviewers and sellers, such as whether they are connected through affiliate programs or other means.
IP Address Analysis: Analysis of IP addresses to identify reviewers who are using the same IP address to post reviews for multiple products.
Temporal Analysis: Examination of the timing of reviews to identify coordinated posting patterns.
Sentiment Correlation: Identification of correlations between review sentiment and purchase behavior.

Technical Considerations:

Endpoint Design: The endpoint should accept a reviewer ID or a product ID as a parameter and return a JSON object containing the related reviews and purchase patterns.
Graph Database: Consider using a graph database to store and analyze the relationships between reviewers, products, and sellers.
Machine Learning: Implement machine learning algorithms to detect suspicious patterns and anomalies.

Development Process

To ensure we deliver high-quality API endpoints that meet the needs of our Amazon integrity analysts, we'll follow a structured development process.

1. Requirements Gathering

We'll start by gathering detailed requirements for each endpoint. This will involve working closely with the analysts to understand their specific needs and use cases. We'll document the required data points, performance expectations, and any specific business rules.

2. API Design

Next, we'll design the API endpoints, including the request parameters, response format, and error handling. We'll follow RESTful principles and ensure the APIs are easy to use and understand. We'll also consider versioning to allow for future enhancements without breaking existing integrations.

3. Development

During the development phase, we'll write the code to retrieve, process, and format the data for each endpoint. We'll use appropriate programming languages, frameworks, and tools to ensure the APIs are efficient, scalable, and secure. We'll also implement comprehensive unit tests to verify the functionality of each endpoint.

4. Testing

Once the development is complete, we'll conduct thorough testing to ensure the APIs meet the requirements and perform as expected. This will include functional testing, performance testing, and security testing. We'll also work with the analysts to conduct user acceptance testing (UAT) to ensure the APIs meet their needs.

5. Deployment

After successful testing, we'll deploy the APIs to a production environment. We'll follow established deployment procedures and monitor the APIs closely to ensure they are running smoothly. We'll also provide documentation and training to the analysts to help them use the APIs effectively.

Technical Stack

To build these API endpoints, we'll leverage a combination of technologies that align with our existing infrastructure and development practices. Here’s a likely stack:

Programming Language: Python (due to its rich ecosystem of data science and web development libraries).
Framework: Flask or Django (for building the API endpoints).
Database: PostgreSQL (for storing structured data).
Cache: Redis or Memcached (for caching frequently accessed data).
Message Queue: Kafka or RabbitMQ (for asynchronous processing of data).
Cloud Platform: AWS (utilizing services like EC2, Lambda, API Gateway, S3, etc.).

Challenges and Considerations

While developing these API endpoints, we anticipate several challenges that we'll need to address proactively:

Data Integration: Integrating data from multiple sources can be complex and time-consuming. We'll need to carefully plan the data integration process and ensure data consistency and accuracy.
Performance: Retrieving and processing large volumes of data can impact performance. We'll need to optimize the APIs for speed and scalability.
Security: Protecting sensitive data is critical. We'll need to implement robust security measures to prevent unauthorized access and data breaches.
Scalability: The API endpoints need to be able to handle increasing traffic and data volumes as the platform grows. We'll need to design the APIs with scalability in mind.

Conclusion

Developing these API endpoints is a crucial step in empowering our Amazon integrity analysts to make informed decisions. By providing comprehensive data on reviewer history, product history, and related reviews/purchase patterns, we can help them identify and address integrity issues more effectively.

Remember, teamwork makes the dream work! Let's collaborate, communicate, and conquer this task together. Go team!