The Art of Search Ranking: Behind the Scenes at Avito
October 3, 2024, 11:50 pm
In the bustling digital marketplace of Avito, search ranking is akin to a finely tuned orchestra. Each element plays a crucial role in delivering the right results to users. With millions of listings and thousands of queries per second, the challenge is monumental. This article delves into the intricacies of Avito's search ranking system, revealing the technologies and strategies that ensure users find what they seek amidst the chaos.
At the heart of Avito's search engine lies a user journey that begins with a simple query. Picture a shopper entering a vast bazaar, seeking a specific item. The first step is typing a request, like "Buy iPhone." As the user types, Avito's system anticipates their needs, suggesting categories and models. This is the first brushstroke in the painting of search results.
Once the user hits "search," they enter a world of listings. But not all queries yield fruitful results. A staggering 25% of searches result in either sparse listings or none at all. This is where Avito's commitment to completeness comes into play. When a user encounters a barren search, the system employs vector-based searches to suggest similar items, breathing life into empty results.
However, ensuring a rich search experience is not without its hurdles. The sheer volume of data—over 200 million listings—poses a significant challenge. Each query must be processed swiftly, balancing the needs of diverse users. Some seek broad options, while others hunt for specific items in particular regions. The system must cater to both, ensuring relevance and diversity in results.
Quality is paramount. Avito's search ranking criteria hinge on four pillars: relevance, conversion potential, fairness to sellers, and speed of sale. Each listing must resonate with the user's query, enticing them to click. Sellers are encouraged to provide detailed descriptions and high-quality images, enhancing the likelihood of conversion. Fairness ensures that no single seller dominates the spotlight, maintaining a level playing field.
The ranking pipeline is a complex machine. It operates in four stages, each designed to refine the results. First, candidates are selected based on a reverse index of the query. Next, an initial ranking filters down to the top 500 listings, considering factors like relevance and freshness. The final stage incorporates paid listings, ensuring that those who invest in promotion gain visibility.
Machine learning plays a pivotal role in this ecosystem. Avito employs gradient boosting models to assess relevance, trained on meticulously curated data. The process involves human annotators who evaluate whether a listing meets the user's needs. This feedback loop is essential for continuous improvement, ensuring that the system evolves alongside user behavior.
Yet, the journey doesn't end there. The search ranking system must also account for the myriad of user types. From individual sellers to large companies, each group has distinct needs. Buyers desire a plethora of options, while sellers seek quick sales. Balancing these interests is a delicate dance, requiring constant monitoring and adjustment.
In addition to search ranking, Avito is also tackling the challenge of node degradation in its Kubernetes clusters. With thousands of servers, maintaining optimal performance is critical. The introduction of Auto Healing mechanisms aims to automate the detection and resolution of issues. This proactive approach minimizes downtime and enhances service reliability.
The Node Problem Detector (NPD) is a key player in this initiative. By replacing a custom-built service with NPD, Avito has streamlined its monitoring processes. NPD offers built-in checks and metrics, allowing for real-time insights into node health. This shift not only reduces resource consumption but also enhances the overall efficiency of the system.
Custom checks are crafted to address specific issues, such as monitoring the health of BGP peers in the network. These checks are written in Go, ensuring they run efficiently without straining system resources. The result is a robust monitoring framework that provides clarity and control over the infrastructure.
In conclusion, Avito's search ranking and infrastructure management are a testament to the power of technology in enhancing user experience. The intricate dance of algorithms, machine learning, and proactive monitoring creates a seamless environment for buyers and sellers alike. As the digital marketplace continues to evolve, Avito remains committed to refining its systems, ensuring that every search leads to a satisfying discovery. The art of search ranking is not just about algorithms; it's about understanding the user, anticipating their needs, and delivering results that resonate.
At the heart of Avito's search engine lies a user journey that begins with a simple query. Picture a shopper entering a vast bazaar, seeking a specific item. The first step is typing a request, like "Buy iPhone." As the user types, Avito's system anticipates their needs, suggesting categories and models. This is the first brushstroke in the painting of search results.
Once the user hits "search," they enter a world of listings. But not all queries yield fruitful results. A staggering 25% of searches result in either sparse listings or none at all. This is where Avito's commitment to completeness comes into play. When a user encounters a barren search, the system employs vector-based searches to suggest similar items, breathing life into empty results.
However, ensuring a rich search experience is not without its hurdles. The sheer volume of data—over 200 million listings—poses a significant challenge. Each query must be processed swiftly, balancing the needs of diverse users. Some seek broad options, while others hunt for specific items in particular regions. The system must cater to both, ensuring relevance and diversity in results.
Quality is paramount. Avito's search ranking criteria hinge on four pillars: relevance, conversion potential, fairness to sellers, and speed of sale. Each listing must resonate with the user's query, enticing them to click. Sellers are encouraged to provide detailed descriptions and high-quality images, enhancing the likelihood of conversion. Fairness ensures that no single seller dominates the spotlight, maintaining a level playing field.
The ranking pipeline is a complex machine. It operates in four stages, each designed to refine the results. First, candidates are selected based on a reverse index of the query. Next, an initial ranking filters down to the top 500 listings, considering factors like relevance and freshness. The final stage incorporates paid listings, ensuring that those who invest in promotion gain visibility.
Machine learning plays a pivotal role in this ecosystem. Avito employs gradient boosting models to assess relevance, trained on meticulously curated data. The process involves human annotators who evaluate whether a listing meets the user's needs. This feedback loop is essential for continuous improvement, ensuring that the system evolves alongside user behavior.
Yet, the journey doesn't end there. The search ranking system must also account for the myriad of user types. From individual sellers to large companies, each group has distinct needs. Buyers desire a plethora of options, while sellers seek quick sales. Balancing these interests is a delicate dance, requiring constant monitoring and adjustment.
In addition to search ranking, Avito is also tackling the challenge of node degradation in its Kubernetes clusters. With thousands of servers, maintaining optimal performance is critical. The introduction of Auto Healing mechanisms aims to automate the detection and resolution of issues. This proactive approach minimizes downtime and enhances service reliability.
The Node Problem Detector (NPD) is a key player in this initiative. By replacing a custom-built service with NPD, Avito has streamlined its monitoring processes. NPD offers built-in checks and metrics, allowing for real-time insights into node health. This shift not only reduces resource consumption but also enhances the overall efficiency of the system.
Custom checks are crafted to address specific issues, such as monitoring the health of BGP peers in the network. These checks are written in Go, ensuring they run efficiently without straining system resources. The result is a robust monitoring framework that provides clarity and control over the infrastructure.
In conclusion, Avito's search ranking and infrastructure management are a testament to the power of technology in enhancing user experience. The intricate dance of algorithms, machine learning, and proactive monitoring creates a seamless environment for buyers and sellers alike. As the digital marketplace continues to evolve, Avito remains committed to refining its systems, ensuring that every search leads to a satisfying discovery. The art of search ranking is not just about algorithms; it's about understanding the user, anticipating their needs, and delivering results that resonate.