Hello, everyone. Thank you for joining today. I am Vedant Agarwal, a senior software engineer working on machine learning at Walmart Global Tech. Today, I am excited to talk about mastering real time personalization, innovations, and neural ranking architectures. In this session, we are going to explore how new breakthroughs in neural ranking are changing the game for real time personalization, especially in e commerce. We will cover everything from embedding based indexing. to attention driven models and see how these strategies improve accuracy, reduce latency and boost conversion rates. Let us jump in and look at how these tools can help us create better user experiences and achieve stronger business results. In this presentation, we will start by understanding the challenges in real time personalization, identifying the complexities that need to be addressed. Next, we'll focus on enhancing accuracy, diving into strategies that ensure recommendations align perfectly with user intent. We will then look at reducing latency. where scalable solutions enable faster and more responsive systems. After that, we will explore how these efforts contribute to boosting conversions, turning personalization into tangible business results, and finally, we will wrap up with conclusions summarizing the critical takeaways and actionable insights. Let's get started. Let us take a look at the challenges that many e commerce businesses face when it comes to a real time personalization. First, there is high latency, which slows down the user experience. In online shopping, Users expect fast responses. If the system takes too long to give recommendations, it disrupts their shopping experience. This delay can frustrate customers and cause them to leave the site, which affects both engagement and sales. Second, inaccurate recommendations are a big issue. Personalization works best when it's accurate. If the suggestions aren't right, users may stop trusting the platform and feel like it doesn't answer their needs. needs. Third, poor personalization leads to lower conversion rates. If recommendations are not aligned with what the user likes or wants, it means missed sales. Personalizing experiences is key to driving conversions and building customer loyalty. Without it, businesses could see fewer repeat customers and lower satisfaction. Lastly, Businesses need solutions that can scale. As they grow, the amount of data increases. Personalization systems need to handle this data efficiently without losing speed or accuracy. This means building strong systems that can work with large data sets while still being quick and responsive. Addressing these issues is crucial for any e commerce platform that wants to improve its personalization. By reducing delays, Improving accuracy, increasing conversion rates, and scaling effectively, businesses can create better experiences for customers and drive growth. We will dive into some strategies and innovations that tackle these challenges and help businesses master real time personalization. Let us look at three advanced strategies to improve accuracy in real time personalization. First, multi tower architecture splits the retrieval and ranking process into two parts, making the system more scalable. Tower 1 handles retrieval, narrowing down a large pool of options based on general relevance. Tower 2 then refines these options by ranking them based on user preferences and other contextual signals. For example, Tower 1 might pull up 100 products, and Tower 2 ranks them based on things like the user's browsing history, making the recommendations more personalized and accurate. Next, semantic search goes beyond just keyword matching by using embeddings and vector similarity to better understand the user's intent. This helps the system find deeper connections between what the user searches for and the items available. For example, If someone searches for comfortable office chairs, the system can recommend ergonomic chairs or those with memory form, even if the exact words are not a perfect match. This helps users find exactly what they need. Lastly, transformer based models like BERT or T5 are great for understanding complex queries and product descriptions. These models analyze multi layered questions, making sure that search results are highly relevant. For instance, if someone searches for budget friendly laptops with good battery life, These models can suggest the most suitable options by understanding the full context of the query. In the next slides, we will dive deeper into semantic search and transformer based models and see how they can help improve accuracy and boost user satisfaction. Let's talk about how semantic search boosts personalization by focusing on context, relevance, and scalability. First, contextual understanding helps move beyond just keyword matching. Semantic search uses word embeddings to understand the deeper meanings behind what users are searching for. For example, if someone searches for affordable running shoes, the system can recognize the words like budget friendly and economical means the same things. So it can show up products that match the user's intent, even if the exact words don't line up. Next, improved relevance. is a big advantage to a semantic search. By considering things like user intent and context, the system can provide more accurate personalized results. For example, if a user has recently checked out fitness trackers, a search for running gear might show shoes that work well with those trackers. This way, the recommendations feel more in tune with the user's need, making their experience better. Finally, scalability and flexibility makes semantic search a great fit for large systems. It works well with models like multi tower architectures with the first tower retrieves broad matches, and the second defines them based on semantic meaning and context. This setup lets the system manage huge catalogs while still keeping recommendations accurate and relevant even as the dataset grows. By combining these strengths, Semantic Search doesn't just improve accuracy, it adapts to user in real time, making it a must have for modern personalization systems. Let's explore what makes transformer based models so powerful, focusing on their ability to understand context and scale efficiently. First, enhanced contextual understanding. Transformers are great at picking up the subtle relationships and data thanks to their self attention mechanism. Unlike older models, transformer look up every part of a query or input in relation to all the other parts, giving them a deep understanding of context. For example, in a search for affordable noise cancelling headphones, the transformer knows that affordable applies to headphones and that noise cancelling adds a specific feature. This level of detail helps them rank results with amazing accuracy. Second, real time personalization. Transformers can quickly adapt and fine tune their recommendations based on new information. They bring in pre trained knowledge and adjust to user behavior in real time. For instance, if a user switches from looking at fitness gear to home gym equipment, the transformer can update its suggestions right away. And fitness gear can be used to show relevant searches. Third, scalability. New transformer models like this deliberate are designed to be more efficient, reducing the computational load. Techniques like model pruning and quantization help speed up processing, which makes it easier to handle millions of queries without sacrificing performance. This means transformers can deliver fast, scalable personalization, even in real life. What really sets transformers apart is their self attention mechanism, which lets them understand the big picture by capturing global relationships and data, and their ability to handle inputs of different lengths. These features make transformers ideal for solving complex personalization challenges at scale. In this part, we will cover four key strategies. To improve the performance of real time personalization systems with some practical examples. First, vector databases for fast retrieval. Vector databases like Pinecone are great for quickly searching through data using approximate nearest neighbors. This means when a user searches for something like running shoes, The system can quickly match the result with product data, ensuring fast and accurate results. Model optimization techniques help make models smaller and faster without losing accuracy. Methods like quantization, pruning, and knowledge distillation make the models more efficient. For example, a recommendation engine that's optimized using quantization can make faster predictions even on mobile devices with limited resources. Caching strategies improve performance for data that gets accessed a lot, like user profiles or popular items. By caching this data and using edge computing, recommendations can be delivered from servers closer to the user, cutting down response time. For example, during a flash sale, caching ensures that users in different regions get fast updates on trending deals, giving them data and keeping them engaged. Lastly, batch processing for inference groups multiple queries together to optimize resources, especially when using GPUs or TPUs. This is useful during busy times when there are a lot of queries as it reduces the processing load and helps keep response times low. I'll go into more detail on vector databases and caching strategies in the next slide. Let's take a closer look at how vector databases drive real time personalization with speed and scalability. First, efficient similarity search helps the system quickly find items that are similar to a user's search. For example, if someone searches for ergonomic chairs, the system can quickly match them with products that have key features like lumbar support or adjustable height. Next, scalability with advanced indexing ensures that searches stay fast, and the system can quickly find items that have key features like lumbar support or adjustable height. Next, scalability with advanced indexing ensures that searches stay fast, and the system Even with millions of items in the database techniques like hierarchical navigable small world helps the system manage large amounts of data efficiently so it can provide quick results even during busy times like sales integration with the ranking models make the systems even better. After the system retrieves similar items, ranking models fine tune the recommendations based on things like a user's preference or purchase history, making the results more personalized and accurate. Finally, cost effective deployment ensures that these databases are optimized for performance without wasting resources, whether they are in the cloud or on premise. They balance speed and cost, making it possible to deliver real time personalization at scale. All these features make vector databases crucial for providing fast, accurate, and adaptive recommendations that help businesses deliver impactful results. Let's look at how caching strategies can boost performance and scalability. First, reduce repeated computations by caching data that's accessed often like embeddings, user profiles, or query results. For example, in a product recommendation system, popular items are often queried. By caching these results, the system avoids recalculating them each time, cutting down on latency and improving efficiency. Next, dynamic cache updates ensures the cached data stays accurate and up to date. With smart invalidation and refresh policies, the system can quickly update information, like trending products during a flash sale, so that recommendations always reflect the latest data. The layered caching approach takes this further by using multiple layers of caching. For example, in memory databases provide super fast data retrieval. Message queues help manage data flow and distributed caching solutions allow large scale data sharing. This setup ensures that The system performs well across all parts of architecture. Finally, scalable caching solutions are key for handling high query volumes. Tools that are optimized for scalability can handle millions of requests, ensuring that the system stays reliable even during peak traffic times. For instance, during a big sale, scalable caching ensures personalized recommendations are served instantly to millions of users. By using these caching strategies, real time systems can cut down on latency, scale efficiently, and keep personalization recommendations accurate and up to date. In this slide, we are going to look at four key strategies. That boost user engagement. Let us break each one down with examples. First, behavioral embeddings capture user actions like clicks, purchase history, and browsing patterns. These embeddings help the system understand user preferences and predict what they might want next. For example, if a user Often looks at sports gear, the recommendations will focus on related products like gym equipment, protein shakes, or running shoes. Next, neural networks enhance catalog data by adding helpful tags, descriptions, and keywords, making products easier to find. For instance, a fashion retailer Tag a plain white shirt as summer wear, formal attire, or office essential based on its features. Similarly, a grocery store could label items with tags like organic, low sodium, or family pack, helping users find products that match their preference. Hybrid text based and semantic retrieval combines traditional keyword search with power of semantic search. While keyword search look for exact matches, semantic search understands the meaning behind queries. For example, a search for energy efficient refrigerators might not include size or features explicitly, but semantic search will ensure that results show products that are energy efficient and compact. This hybrid approach gives precise and relevant results based on what the user really needs. Finally, Dynamic feature pipelines with streaming data use tools like Apache Kafka or Apache Flink to process user actions in real time, such as clicks and views. This helps update features like trending items or recently viewed products instantly, ensuring that the system stays fresh and relevant, even as user behavior changes frequently. These four strategies are the backbone of effective systems. In the next slide, we will dive deeper into behavioral embeddings and dynamic feature pipelines. Let us explore how embeddings based strategies by using the examples of a user who's interested in fitness to keep things consistent. First, embedding generation. When a user browses fitness related products like sports gear or buys protein shakes, neural networks can create high dimensional vectors or embeddings to represent their preferences. These embeddings capture the user's fitness focused behavior, helping the system identify them as someone into health and wellness. Second, real time adaptation ensures embedding updates as the user's interests shift. For example, If the user starts browsing running shoes and then switches to yoga mats, the system adjusts the embedding to reflect the new interest in yoga. As a result, recommendations will now focus on yoga related products like resistance band or meditation cushions. Intent prediction builds on this by using embeddings to predict what the user might need next. Based on the browsing history of running shoes and yoga mats, the system might predict an interest in fitness accessories such as water bottles, activity trackers, and suggest these as the next things to check out. Cross session learning ensures that the system keeps track of the user's preference across visits. If the user comes back after a few weeks and starts searching for home workout gear, the system remembers their fitness interest and recommends items like dumbbells or assistance machines, staying relevant even over time. Finally, Seamless integration with vector databases like Pinecone helps. Quickly retrieve these embeddings. For instance, when the user searches for training gear, the system matches their fitness related embeddings with relevant product embeddings, suggesting items like durable training shoes or compact gym equipment that fits their preference. These strategies can be used to Work together to provide real time, adaptive, and consistent personalization, making the users feel understood and engaged at every step of the way. In this section, we will look at how dynamic feature pipelines enable real time personalization using a flash sale as an example to show their impact. First, real time data ingestion is important. Captures user actions like clicks, views, and purchases as they happen. Tools like Apache Kafka or AWS Kinesis stream these events in real time, ensuring that as users browse and interact with products during the flash sale, no data is missed. Next, Feature Transformation and Enrichment turns this raw data into useful insights. Tools like Apache Flink or Spark Streaming processes the data by applying transformations, like generating embeddings or using time decay functions. For examples, products that have received a lot of recent clicks or views during the sale are prioritized, so the hottest items get more visibility in recommendations. Then, we have contextual feature updates, which adjusts user preferences and product ranking in real time. As users engage with the site, features like session recency, Our trending items are updated. For example, if a product becomes super popular during the flash sale, the system immediately reflects that in its recommendations, ensuring that users are always seeing the most relevant items. Finally, model integration for real time predictions uses the updated data to feed into deployed models like TensorFlow Serving or Triton, which Generate personalized recommendations on the fly. This means the system can suggest the best product for each user based on their behavior during the sale. Together, these components ensure that the system stays adaptive, relevant, and capable of handling high demand solutions like flash sales while delivering personalized results to users. Let us quickly summarize the key elements of a scalable system for real time personalization. First, scalable data pipeline architecture ensures that system can handle millions of user interactions like clicks, views, and purchases in real time. This is especially important during high traffic events like flash sales, where the system must remain fast and responsive even during heavy load. Next, Vector Database Integration enables fast and accurate similarity searches. By matching user preferences with product features, these databases help deliver relevant recommendations in real time. Dynamic Feature Engineering is another piece. It allows the system to update features such as session, recency, or trending items on the fly. This ensures the system can adapt quickly to real time changes in user behavior. Finally, A B testing and monitoring frameworks allow businesses to continuously refine their recommendations by testing different strategies and tracking metrics like latency and conversion rates. The system can be regularly optimized to improve user engagement. Together, these components create a powerful scalable framework that supports personalization at scale, ensuring the system remains precise, adaptive, and continuously improving. With all of this, we have the question of why is it that we must innovate to make our recommendations better? Personalization has shifted from a nice to have to a must have driven by evolving user expectations. Today's users want real time, highly relevant experiences that adjust to their behavior. Anything less leads to disengagement and missed opportunities. Scalability challenges make this difficult. Traditional systems struggle with the massive amounts of data and the precision needed for hyper personalized experiences. As user interactions become more complex, these limitations become obstacles for businesses trying to stay competitive. That's where AI infrastructure conversion comes in. New technologies like advanced neural networks, vector databases, and dynamic feature pipelines are transforming how do we do personalization. These innovations Allow systems to process large amounts of data quickly, adapt to user behavior in real time, and offer recommendations with unmatched accuracy. The business impact is huge. Real time personalization increases user engagement, drives conversions, and builds loyalty. Businesses that adopt these technologies are positioning themselves to lead in a competitive market where user satisfaction is key to long term success. Altogether, these factors show that real time personalization isn't just optional anymore. It's essential for growth and staying relevant in today's digital world. Let's summarize the key takeaways for mastering real time personalization. Personalization is the future. It is driven by advanced neural ranking models and is crucial for meeting user expectations. In today's fast paced digital world, users expect instant, relevant experiences. Anything less risks losing their attention. Second, innovation drives results. Technologies like Vector Database, Dynamic Feature Pipeline, and Scalable Microservices are changing the game. These innovations improve accuracy, reduce latency, and boost conversion, proving their importance in modern systems. Next, seamless integration is the key. Combining AI models with solid software engineering ensures systems are scalable, adaptable, and sustainable. A well integrated system can meet current demands and evolve with user needs and new technologies. Finally, stay ahead. Embracing these advanced tech strategies give businesses a competitive edge by providing highly relevant user experiences. Companies that invest in these technologies are better positioned to retain the users. foster loyalty, and achieve long term success. Achieving these strategies highlights the importance of innovation, integration, and experimentation in shaping the future of personalization. By embracing these principles, we can not only meet the demands of today's users, but also stay ahead of the curve in an ever evolving digital landscape. That is all from the presentation today. Thank you for all of your time. I hope this session gave you some valuable insights into mastering real time personalization and how it can transform modern systems. If you would like to continue the conversation or share ideas, feel free to connect with me on LinkedIn. I'm always up for connecting with like minded professionals and discussing new approaches in AI, machine learning, and software engineering. Let's stay connected and keep learning from each other. Thanks again.