Ranking Systems from Scratch: Tips and Best Practices

Abstract

This article provides a comprehensive guide on developing ranking systems from scratch, covering best practices and tips for each stage of the process. Ranking systems are crucial in information retrieval, ensuring that users receive the most relevant search results. The article emphasizes the importance of a systematic and iterative approach, starting with ensuring data quality and availability. It discusses the significance of having a complete dataset that includes key user behavior metrics such as impressions, clicks, add-to-cart actions, and orders. The development process begins with simple rule-based systems, which rank items based on historical performance metrics like the number of orders or clicks. The article then progresses to more advanced heuristic models that consider multiple factors and assign weights to each factor based on their perceived importance. As the ranking system becomes more complex and data-rich, the article suggests transitioning to machine learning models, such as logistic regression, linear regression, gradient-boosted trees, and neural networks. It also highlights the importance of feature engineering, online experimentation, and monitoring key performance metrics to optimize the ranking system\'s performance. The article concludes by emphasizing the need for a step-by-step approach, ensuring data quality, and continuously monitoring and refining the models to deliver the most relevant search results to users.

Introduction

I. INTRODUCTION

Ranking systems play a vital role in information retrieval, ensuring users receive the most relevant and meaningful search results. These systems determine the order in which results are presented by assigning relevance scores to documents based on user queries. Creating effective ranking systems from the ground up can be quite challenging, with options ranging from basic rule-based methods to more sophisticated machine-learning models.

Developing a strong ranking system requires a systematic and iterative approach. It's important to ensure that user behavior data is readily available and of high quality. We can start with simple rule-based systems and then gradually move on to more advanced models. It is important to incorporate feature engineering, online experimentation, and monitoring of key metrics into the development process.

In this article, we will delve into important tips and best practices for creating ranking systems from scratch. Let's talk about the significance of data quality, the evolution from basic rule-based systems to heuristic models, and the shift towards machine learning techniques like logistic regression, gradient-boosted trees, and neural networks. By following these guidelines, organizations can create ranking systems that provide highly relevant search results and improve user satisfaction.

II. ENSURING DATA QUALITY AND AVAILABILITY

To create a ranking system, it's important to have a complete dataset that covers different aspects of user behavior on the product. This will help ensure that the data is accurate and readily available. It would be great if this dataset could include the following key components:

1. Impressions: Keep track of how many times each item appears. Each item on a search results page that has 10 things and gets 1,000 views would have 1,000 impressions.
2. Clicks: Record the number of clicks each document or item receives from users. If an item is clicked 100 times out of 1,000 impressions, it would have a click-through rate (CTR) of 10%.
3. Add-to-cart Actions: Monitor the number of times users add each item to their shopping cart. This indicates a higher level of interest compared to clicks. For instance, if an item is added to the cart 50 times out of 1,000 impressions, it would have an add-to-cart rate of 5%.
4. Orders: Track the number of successful purchases for each item. This is the strongest signal of relevance and user satisfaction. If an item is purchased 25 times out of 1,000 impressions, it would have a conversion rate of 2.5%.

Here's an example of what the data could look like

It's important to have clearly defined datasets for training, testing, and evaluation, in addition to collecting this data. It's a common practice to divide the data into three parts: 70% for training, 15% for testing, and 15% for evaluation. This split allows for the development and tuning of the ranking system using a representative sample of user behavior data while also enabling unbiased performance assessment on unseen data.

Let's say we have a dataset with 100,000 user interactions. We can split it in the following way:

• Training dataset: 70,000 interactions
• Testing dataset: 15,000 interactions
• Evaluation dataset: 15,000 interactions

By maintaining our dataset well organized and managing it with precision, along with extensive testing and evaluation, we ensure the accuracy and reliability of the data used in creating and validating our ranking system. By building a solid base, we can analyze data efficiently and consistently improve the performance of the ranking algorithm.

III. STARTING WITH A SIMPLE RULE-BASED SYSTEM

When you begin building a ranking system, it's helpful to start with a simple rule-based approach. This allows you to establish a baseline and gain valuable insights from the data. A popular approach involves ranking documents or items by considering their historical performance, such as the number of orders or clicks they have garnered. This method is straightforward and practical, making it a great choice for beginners.

Imagine we have a dataset of products on an e-commerce platform denoted in a graph:

A simple rule-based ranking system could rank these products based on the number of orders they have received. In this case, the ranking would be:

• P3 (800 orders)
• P5 (600 orders)
• P1 (500 orders)
• P2 (300 orders)
• P4 (200 orders)

Alternatively, we could rank the products based on the number of clicks they have received:

• P3 (3,000 clicks)
• P5 (2,500 clicks)
• P1 (2,000 clicks)
• P2 (1,500 clicks)
• P4 (1,000 clicks)

To implement this rule-based system, we would follow these steps:

• Collect and preprocess the data: Gather the necessary data (e.g., orders and clicks) for each product and ensure it is clean and properly formatted.
• Define the ranking criteria: Decide on the metric(s) to use for ranking, such as the number of orders or clicks.
• Sort the items: Sort the products in descending order based on the chosen ranking criteria.
• Display the ranked results: Provide the users with a sorted list of products in a ranking system-determined order.

Here's a simple Python code snippet that demonstrates the implementation of a rule-based ranking system based on the number of orders:

# Sample dataset

products = [

    {'id': 'P1', 'orders': 500, 'clicks': 2000},

    {'id': 'P2', 'orders': 300, 'clicks': 1500},

    {'id': 'P3', 'orders': 800, 'clicks': 3000},

    {'id': 'P4', 'orders': 200, 'clicks': 1000},

    {'id': 'P5', 'orders': 600, 'clicks': 2500}

]

# Sort products based on the number of orders in descending order

ranked_products = sorted(products, key=lambda x: x['orders'], reverse=True)

# Display the ranked products

for i, product in enumerate(ranked_products, start=1):

    print(f"{i}. {product['id']} ({product['orders']} orders)")

Output

• P3 (800 orders)
• P5 (600 orders)
• P1 (500 orders)
• P2 (300 orders)
• P4 (200 orders)

Starting with a straightforward rule-based ranking system allows us to establish a baseline and gain valuable insights into the data conversationally. This approach can also be used as a basis for more advanced ranking methods, like heuristic models or machine learning-based techniques, that can be incorporated into future iterations of the ranking system development process.

IV. IMPLEMENTING A HEURISTIC MODEL

After implementing a basic rule-based ranking system and collecting user feedback, the next step is to develop a more advanced heuristic model. This model takes into account multiple factors that influence the relevance and popularity of a document or item, such as impressions, clicks, add-to-cart actions, and orders. By assigning hand-tuned weights to each of these factors, we can create a more nuanced and effective ranking formula.

Let's consider an example dataset for an e-commerce platform:

To create a heuristic model, we assign weights to each factor based on their perceived importance. For example:

• Impressions: weight1 = 0.1
• Clicks: weight2 = 0.3
• Add-to-cart: weight3 = 0.5
• Orders: weight4 = 1.0

The ranking score for each product can be calculated using the following formula:

ranking_score = 0.1 * impressions + 0.3 * clicks + 0.5 * add_to_cart + 1.0 * orders

Applying this formula to our example dataset, we get:

Based on these ranking scores, the heuristic model would rank the products in the following order:

• P3 (score: 1,390)
• P2 (score: 1,255)
• P1 (score: 1,250)
• P5 (score: 1,195)
• P4 (score: 760)

Adjusting the weights based on domain knowledge and empirical observations can help optimize the ranking performance. For instance, if we discover that orders are a more reliable measure of relevance than clicks, we could adjust the weighting by increasing the importance of orders and decreasing the significance of clicks.

With the help of a heuristic model, we can develop a more advanced ranking system that considers various indicators of relevance and popularity. This will result in better user satisfaction and engagement.

V. TRANSITIONING TO MACHINE-LEARNING MODELS

As the ranking system becomes more intricate and the amount of data expands, adopting machine-learning models can offer substantial advantages. Machine learning algorithms have the ability to learn patterns and relationships from data, which allows for more accurate and effective ranking compared to rule-based or heuristic approaches.

Using a simple logistic or linear regression model is a great way to begin exploring machine learning-based ranking. These models can include a range of user-related and document-related features to forecast the relevance or popularity of a document or item.

Example features for a machine learning ranking model:

A. User-related Features

• User demographics (age, gender, location)
• User behavior (previous clicks, purchases, ratings)
• User preferences (inferred from browsing history)

B. Document-related Features

• Text-based features (title, description, keywords)
• Categorical features (brand, category, price range)
• Engagement metrics (impressions, clicks, add-to-cart actions, orders)

To train a logistic or linear regression model, it is necessary to have a dataset that contains labeled examples. It's important to include the relevant features and a target variable that indicates the relevance or popularity of the document in each example. It's important to divide the dataset into training, test, and validation sets to ensure the model's effectiveness and avoid overfitting.

Example dataset for a machine learning ranking model:

To train the model, we use the training set and optimize the model's parameters using techniques like gradient descent. The model learns to predict the relevance or popularity of a document based on the input features.

After training, we evaluate the model's performance using the test set. Common evaluation metrics for ranking models include:

• Precision@k: The proportion of relevant documents among the top-k ranked results.
• Recall@k: The proportion of all relevant documents that are present in the top-k ranked results.
• Normalized Discounted Cumulative Gain (NDCG): A measure of ranking quality that considers the position of relevant documents in the ranked list.

If the model performs well on the test set, we can further validate its effectiveness using the validation set, which serves as an unbiased evaluation of the model's performance on unseen data.

VI. FEATURE ENGINEERING

Advanced features play a crucial role in enhancing the performance of ranking models. Here are some examples of sophisticated features that can be incorporated:

A. Result Embeddings

Represent search results as dense vectors that capture their semantic meaning. Use techniques like Word2Vec, GloVe, or BERT to generate embeddings based on the text content of the results.

Example: A search result for "iPhone 12" can be represented as a 100-dimensional vector: [0.2, 0.5, -0.1, ..., 0.8]

B. User Embeddings

Represent users as vectors based on their preferences, behavior, and interaction history. Utilize user clicks, purchases, ratings, and browsing history to create user embeddings.

Example: A user who frequently clicks on electronics products can be represented as a 50-dimensional vector: [0.7, -0.2, 0.4, ..., 0.1]

C. Cross-item Features

Capture relationships and similarities between different documents or items. Generate features like cosine similarity, Jaccard similarity, or co-occurrence frequency between pairs of items.

Example: If users who bought item A also frequently bought item B, create a feature indicating their similarity or co-occurrence.

VII. ONLINE EXPERIMENTATION AND METRICS

To assess the performance and impact of ranking models, conducting online experiments and monitoring relevant metrics is essential. Online experimentation involves exposing different variations of the ranking algorithm to subsets of users and comparing their performance.

A. Key Metrics to Consider

1. Click-through rate (CTR): The proportion of users who click on a search result out of the total number of impressions.
2. Conversion rate: The percentage of users who complete a desired action (e.g., making a purchase) after clicking on a search result.
3. User feedback: Collect explicit feedback from users through surveys or ratings to gauge their satisfaction with the search results.

VIII. ADVANCED MODELS: GRADIENT-BOOSTED TREES AND NEURAL NETWORKS

When logistic or linear regression models have reached their performance limits, exploring more advanced techniques can lead to further improvements in ranking quality.

A. Gradient Boosted Trees (e.g., XGBoost, Light GBM)

1. Combine multiple decision trees to create a powerful ensemble model
2. Automatically capture non-linear relationships and interactions between features
3. Iteratively train trees to focus on difficult examples and improve overall performance

B. Neural Networks (e.g., Deep Learning)

1. Leverage deep neural architectures to learn complex patterns and representations from data
2. Use techniques like convolutional neural networks (CNNs) for text- or image-based features
3. Employ recurrent neural networks (RNNs) or transformers to capture sequential or contextual information
4. Enable end-to-end learning from raw features to relevant scores

Conclusion

Developing ranking systems from the ground up necessitates a methodical and step-by-step approach. Developers can create effective and efficient ranking mechanisms by starting with simple rule-based systems and gradually progressing to more advanced models, all while ensuring data quality. It\'s important to regularly experiment and monitor metrics online to gain a better understanding of how the model behaves and make informed decisions based on data. As the ranking system becomes more complex, techniques such as gradient-boosted trees and neural networks can be used to achieve the best possible performance. Organizations can create effective ranking systems by implementing these tips and best practices. This will ensure that users receive search results that are both relevant and meaningful.

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Copyright © 2024 Hareen Venigalla, Vidhya Suresh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.