{"id":473,"date":"2026-09-29T18:53:46","date_gmt":"2026-09-29T13:23:46","guid":{"rendered":"https:\/\/blogs.infosys.com\/infosys-wingspan\/?p=473"},"modified":"2026-09-29T18:53:46","modified_gmt":"2026-09-29T13:23:46","slug":"smarter-search-smarter-learning-advancing-relevance-with-multi-layer-reranking","status":"publish","type":"post","link":"https:\/\/blogs.infosys.com\/infosys-wingspan\/implementation-of-artificial-intelligence-in-lxp\/smarter-search-smarter-learning-advancing-relevance-with-multi-layer-reranking\/.html","title":{"rendered":"Smarter Search, Smarter Learning: Advancing Relevance with Multi Layer Reranking"},"content":{"rendered":"<p>In today\u2019s digital learning ecosystem, discoverability is everything. Learners are flooded with content \u2014 but only find value when the <em>right<\/em> learning surfaces at the <em>right<\/em> moment. That\u2019s where search relevance becomes critical.<\/p>\n<p>&nbsp;<\/p>\n<h2><strong>Why Elasticsearch Matters in Enterprise Learning<\/strong><\/h2>\n<p>Elasticsearch is a fast, scalable, and widely adopted search engine that sits behind many experiences we rely on daily \u2014 product filters on e\u2011commerce sites, autosuggest in apps, personalized recommendations, and even log dashboards used by engineering teams.<\/p>\n<p>For enterprise learning platforms like <strong>Infosys Wingspan<\/strong>, Elasticsearch plays an equally important role. It helps learners navigate vast libraries containing hundreds of thousands of courses, videos, labs, and certifications. But while the basic information retrieval solution provides speed and scale, enterprises need more than fast results \u2014 they need <em>meaningful, context-aware, <\/em>and<em> highly relevant<\/em> results.<\/p>\n<p>Because when learners can\u2019t quickly locate what they need, engagement drops, outcomes diminish, and the learning experience loses its impact.<\/p>\n<h2><strong>The Challenge: Delivering Relevant Learning Content at Scale<\/strong><\/h2>\n<p>Traditional keyword search often retrieves information that is technically correct \u2014 but not truly helpful. Learners differ by role, proficiency, location, preferred language, and their unique learning journeys. At this scale, relevance cannot be left to chance.<\/p>\n<p>We needed a search system that understands:<\/p>\n<ul>\n<li><strong>Intent<\/strong> \u2014 what the learner is truly looking for<\/li>\n<li><strong>Behavior<\/strong> \u2014 what they tend to engage with<\/li>\n<li><strong>Context<\/strong> \u2014 who they are and where they are in their learning journey<\/li>\n<\/ul>\n<p>This led us to reimagine search ranking through a multi-layered, intelligence-driven approach.<\/p>\n<h2><strong>Our Solution: Multi\u2011Layer Reranking<\/strong><\/h2>\n<p>We built a <strong>5\u2011step reranking pipeline<\/strong> that blends traditional information retrieval, machine learning, personalization, and business logic into a unified ranking strategy.<\/p>\n<h3><strong>1. BM25 Retrieval<\/strong><\/h3>\n<p>Quickly fetches an initial candidate set of relevant results.<\/p>\n<h3><strong>2. Learning to Rank (LTR) with XGBoost<\/strong><\/h3>\n<p>Trained using:<\/p>\n<ul>\n<li>Rich telemetry (clicks, progress, completion)<\/li>\n<li>A Dynamic Bayesian Network (DBN) click model<\/li>\n<li>Metadata and user\u2011behavior signals<\/li>\n<\/ul>\n<p>This allows the algorithm to understand what learners actually find useful.<\/p>\n<h3><strong>3. Trending Content Boosts<\/strong><\/h3>\n<p>Highlights what\u2019s gaining traction across the learner base.<\/p>\n<h3><strong>4. Personalization<\/strong><\/h3>\n<p>Adjusts ranking based on learner\u2011specific attributes \u2014 language, location, history, and interests.<\/p>\n<h3><strong>5. Business Priority Boosts<\/strong><\/h3>\n<p>Elevates strategic programs, certifications, or initiatives when needed.<\/p>\n<p>Each layer incrementally improves precision, ensuring only the most relevant results rise to the top.<\/p>\n<h2><strong>The Data Behind the Model<\/strong><\/h2>\n<p>To train the system effectively, we used:<\/p>\n<ul>\n<li><strong>174,000+ query\u2013document pairs<\/strong><\/li>\n<li><strong>Four\u2011level graded relevance<\/strong><\/li>\n<li>Extensive feature engineering across content metadata and behavioral insights<\/li>\n<\/ul>\n<p>This comprehensive dataset helped our LTR model gain a deep understanding of learner preferences and content utility.<\/p>\n<h2><strong>The Outcomes: Clear, Measurable Impact<\/strong><\/h2>\n<p>Deploying our multi\u2011layer reranking architecture produced significant improvements in learner experience and platform efficiency:<\/p>\n<ul>\n<li><strong>NDCG@10:<\/strong> improved from <strong>0.913 \u2192 0.956<\/strong><\/li>\n<li><strong>MRR@10:<\/strong> more than <strong>2\u00d7 increase<\/strong><\/li>\n<li><strong>Average click position:<\/strong> improved from <strong>5.3 \u2192 4.7<\/strong><\/li>\n<li><strong>More clicks shifted to top results<\/strong><\/li>\n<li><strong>Overall searches increased by 44%<\/strong> over six months<\/li>\n<\/ul>\n<p>In essence, learners found relevant content <em>faster<\/em>, and trust in the search experience increased substantially.<\/p>\n<h2><strong>What\u2019s Next?<\/strong><\/h2>\n<p>We\u2019re already exploring the next frontier of intelligent learning search:<\/p>\n<ul>\n<li><strong>Hybrid retrieval<\/strong> combining BM25 + dense vector embeddings<\/li>\n<li><strong>Reinforcement learning<\/strong> that adapts ranking from ongoing learner feedback<\/li>\n<li><strong>Deeper, real\u2011time personalization<\/strong><\/li>\n<li><strong>Unified search relevance pipelines<\/strong> across all our learning platforms<\/li>\n<\/ul>\n<p>Our vision is simple:<br \/>\n<strong>A search system that continuously learns and evolves\u2014just like our learners.<\/strong><\/p>\n<h2><strong>How This Strengthens Infosys Wingspan<\/strong><\/h2>\n<p>At its core, Infosys Wingspan is designed to deliver <strong>personalized, scalable, enterprise\u2011ready learning experiences<\/strong>. Intelligent search is a cornerstone of this vision.<\/p>\n<p>By integrating multi-layer reranking and continuous learning signals into Wingspan:<\/p>\n<ul>\n<li>Learners discover relevant content quickly<\/li>\n<li>Organizations drive higher adoption, completion, and skill mobility<\/li>\n<li>Learning journeys become intuitive, frictionless, and meaningful<\/li>\n<\/ul>\n<p><strong>Smarter search isn\u2019t just a feature\u2014it\u2019s a competitive advantage.<\/strong><\/p>\n<p>If you\u2019re looking to transform how your workforce learns, discovers, and grows, <strong>Infosys Wingspan is built to propel your enterprise into the future of AI-powered learning.<\/strong><\/p>\n<h2><strong>Dive Deeper<\/strong><\/h2>\n<p>Peer\u2011Reviewed Research: <a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3627217.3627224\">https:\/\/dl.acm.org\/doi\/10.1145\/3627217.3627224<\/a><\/p>\n<p>Talk Recording: <a href=\"https:\/\/www.elastic.co\/events\/elasticon\/archive\/elasticon-bengaluru\/improving-search-relevance-using-multilayer-reranking\">https:\/\/www.elastic.co\/events\/elasticon\/archive\/elasticon-bengaluru\/improving-search-relevance-using-multilayer-reranking<\/a><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In today\u2019s digital learning ecosystem, discoverability is everything. Learners are flooded with content \u2014 [&hellip;]<\/p>\n","protected":false},"author":1030,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"coauthors":[14,31],"class_list":["post-473","post","type-post","status-publish","format-standard","hentry","category-implementation-of-artificial-intelligence-in-lxp"],"acf":[],"_links":{"self":[{"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/posts\/473","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/users\/1030"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/comments?post=473"}],"version-history":[{"count":2,"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/posts\/473\/revisions"}],"predecessor-version":[{"id":478,"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/posts\/473\/revisions\/478"}],"wp:attachment":[{"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/media?parent=473"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/categories?post=473"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/tags?post=473"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.infosys.com\/infosys-wingspan\/wp-json\/wp\/v2\/coauthors?post=473"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}