# personalisation Published articles for personalisation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Vinted Serves Personalised Search Autocomplete DevFeed: [How Vinted Serves Personalised Search Autocomplete]() Original publisher: [Read original article]() Author: Justina Bartulevičienė Published: 2026-04-22T00:00:00Z Content type: article Language: en Sources: [Vinted]() Topics: [recommendation systems](), [data](), [systems](), [Learning]() Tags: [autocomplete](), [model](), [offline](), [performance](), [personalisation](), [pipeline](), [search]() ### AI overview Vinted describes the architecture and operation of its personalized search autocomplete service. The system generates candidate queries from product metadata and search logs, indexes and matches them with typo tolerance, and ranks suggestions using personalization and a Learning-to-Rank model. The service supports 24 languages and more than 50 country-language combinations, with 125 million queries and reported throughput of 4,700 queries per second at 31 ms P99. ### Source excerpt At Vinted, more than 20% of all search sessions now start with a click on an autocomplete suggestion. A few years ago, that number was below 8%. Autocomplete not only saves typing effort - it helps people discover listings they didn't know existed, and guides them toward successful searches. Today, across 24 languages and 50+ country-language combinations, we have a pool of 125 million different queries ready to suggest to users. Our service, svc-suggestions, runs on Vespa and matches and ranks 4,700 queries per second at 31 ms P99. ## Lumos: Inside Dream11's Leap from Task-Based Models to Foundational Intelligence DevFeed: [Lumos: Inside Dream11's Leap from Task-Based Models to Foundational Intelligence]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-01-22T06:40:39Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [foundation-models](), [Sports](), [Machine learning](), [Large language models (LLMs)]() Tags: [analysis](), [competition](), [context](), [dream11](), [fragmentation](), [incremental](), [llm](), [ml](), [models](), [notifications](), [personalisation](), [scale](), [sports](), [systems](), [tech]() ### AI overview Dream11 describes Lumos, a foundation model for personalisation that connects user behaviour, context, and changing interests across sports experiences. The article reports a 2.5% lift in ROC AUC and a 4.6% reduction in MAPE across key tasks, while replacing dozens of task-specific systems with a single scalable foundation. ### Source excerpt By Dhruv Nigam At Dream11, our mission to 'make every match more exciting' starts with a simple truth: every fan experiences sport differently. Some users show up for marquee matches, while others engage consistently across the season. Some enjoy deep analysis; others come for emotion, banter, and shared moments. Even how fans prefer to be spoken to -- through in-app communication or notifications -- varies, from playful and expressive to direct and informational. In sports, context changes everything. A quiet weekday feels very different from the eve of a knockout match, and behaviour shifts with formats, rivalries, and the stage of competition. Personalisation at Dream11 therefore goes beyond surface-level customisation -- it's about understanding fans in motion and how their interests evolve. We've long recognised this challenge, but understanding and acting on these signals across millions of users, each with their own patterns and preferences, is far from easy. Over time, it became clear that small, incremental ML enhancements wouldn't get us where we needed to go. To stay truly user-first, we needed a system that could connect behaviour, context, and past, present, and future moments, all at once. That realisation led us to a ground-up rethink of how we build models at Dream11, and eventually, to Lumos -- our foundation model for personalisation. Lumos helped deliver a 2.5% lift in ROC AUC (Area Under the Receiver Operating Characteristic Curve) and a 4.6% reduction in MAPE (mean absolute percentage error) across key tasks, significantly improving personalisation, while replacing dozens of task-specific systems with a single, scalable foundation.The Problem: When Task-Based Models Stop Scaling For a long time, our personalisation stack relied on 50+ small, specialised models, each designed to understand a narrow aspect of user behaviour. Some models focused on sports affinity, others on language preferences or communication style. While these were effective in iso ## Location-Based Fashion Recommendations at Myntra DevFeed: [Location-Based Fashion Recommendations at Myntra]() Original publisher: [Read original article]() Author: Siddhartha Devapujula Published: 2024-04-22T11:25:37Z Content type: tutorial Language: en Sources: [Myntra]() Topics: [recommendation systems](), [Machine learning](), [recommendations]() Tags: [artificial-intelligence](), [ecommerce](), [fashion](), [location-based](), [machine-learning](), [personalisation](), [recommendation-system](), [recommendation-systems](), [search]() ### AI overview Myntra explains how machine-learning recommendation systems can use location as user side information to improve personalized fashion recommendations, including for cold-start users. ### Source excerpt Authored By Rohit Gupta & Siddhartha Devapujula Introduction Millions of users visit Myntra daily to upgrade their wardrobes and millions of items are listed on the platform at any given time. Users neither have the time nor the capability to scroll through this vast list of items. Even after applying category and attribute filters, usually the number of items is still in thousands. Hence it becomes critical that the top search results for any user are both relevant and personalized. Just like search, many other recommendation widgets across the platform face the same challenges. Fashion Diversity -- Every Region has its own Fashion Showing each user the best styles for them from a catalog of million plus products is where machine learning based recommendation systems come into play. From search results on google to your netflix home screen, recommendation systems are working in the background to get you the best results. It is impossible to imagine modern age internet experience without these systems. The uber goal of these models is to take the user features and the vast list of items as input ,and generate a small personalized list of items for each user. For these systems to work, we mainly use the user's historical activity on the platform. In this blog we will see how using other kinds of user details can also enhance the quality of recommendations. In the next sections, we dive into the details of recommendation systems and related techniques. We explain the motivation for a location based recommendation system and how we built one at Myntra. Later we discuss a few use cases at Myntra, results and potential future work. Basics of recommendation systems This is a very simple read about recommendation systems by Google -- Recommendations: What and Why? | Machine Learning | Google for Developers. Readers can skip if they are already aware of this. Traditional recommendation models focus on using the user's historical interactions on the platform to learn. This wor ## Designing a faster data model to personalize browsing in real time DevFeed: [Designing a faster data model to personalize browsing in real time]() Original publisher: [Read original article]() Author: Jordi Villar Published: 2022-05-11T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [API](), [data](), [real-time]() Tags: [api](), [data](), [personalisation](), [real-time](), [the-data-base]() ### AI overview The article discusses designing a faster data model and optimizing API endpoint performance to personalize browsing in real time. ### Source excerpt Optimizing API endpoint performance for browsing personalisation.