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      <title>Agoda Builds Multimodal Content System to Bridge Images and Reviews in Travel Discovery</title>
      <link>https://www.infoq.com/news/2026/05/agoda-multimodal-content-system/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Low+Latency-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/agoda-multimodal-content-system/en/headerimage/generatedHeaderImage-1778985448660.jpg"/&gt;&lt;p&gt;Agoda Multimodal Content System&lt;/title&gt;&lt;link&gt;https://example.com/agoda-multimodal-content-system&lt;/link&gt;&lt;description&gt;Agoda unifies hotel images and guest reviews using a shared topic taxonomy, enabling multimodal retrieval across 700M+ images and multilingual reviews with offline enrichment and low-latency serving.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Low Latency</category>
      <category>Multi-Model Databases</category>
      <category>Spark</category>
      <category>Search</category>
      <category>Enterprise Content Management</category>
      <category>Architecture</category>
      <category>Machine Learning</category>
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      <pubDate>Tue, 19 May 2026 14:29:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/agoda-multimodal-content-system/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Low+Latency-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-05-19T14:29:00Z</dc:date>
      <dc:identifier>/news/2026/05/agoda-multimodal-content-system/en</dc:identifier>
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      <title>Swiggy Improves Search Autocomplete Using Real Time Machine Learning Ranking</title>
      <link>https://www.infoq.com/news/2026/05/swiggy-autocomplete-rt-ranking/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Low+Latency-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/swiggy-autocomplete-rt-ranking/en/headerimage/header-1778975533066.jpg"/&gt;&lt;p&gt;Swiggy detailed real-time machine-learning ranking system for autocomplete built on OpenSearch. The architecture separates candidate generation and ranking, uses feature stores for real time signals, and applies learning to rank models for improved relevance. It replaces heuristic ranking while maintaining strict latency constraints and enabling continuous model updates from user behavior signals.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>OpenSearch</category>
      <category>learning</category>
      <category>Low Latency</category>
      <category>Real Time</category>
      <category>Rankings</category>
      <category>Search</category>
      <category>Infrastructure</category>
      <category>Event Driven Architecture</category>
      <category>Machine Learning</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
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      <pubDate>Mon, 18 May 2026 14:38:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/swiggy-autocomplete-rt-ranking/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Low+Latency-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-05-18T14:38:00Z</dc:date>
      <dc:identifier>/news/2026/05/swiggy-autocomplete-rt-ranking/en</dc:identifier>
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