<?xml version="1.0" encoding="utf-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>The Tech Gazette · Papers · Search and retrieval</title>
    <link>https://techgazette.com.br/research/#retrieval</link>
    <atom:link href="https://techgazette.com.br/research/retrieval/feed.xml" rel="self" type="application/rss+xml"/>
    <description>This week's papers in Search and retrieval, each with what its authors did and found, in sentences from its own abstract.</description>
    <language>en</language>
    <ttl>180</ttl>
    <lastBuildDate>Fri, 16 Oct 2026 12:00:00 GMT</lastBuildDate>
    <item>
      <title>Learning Popularity-Aware User Representations for Sequential Recommendation via Retrieval-Augmentation</title>
      <link>https://dl.acm.org/doi/10.1145/3858619</link>
      <guid isPermaLink="true">https://dl.acm.org/doi/10.1145/3858619</guid>
      <pubDate>Fri, 16 Oct 2026 12:00:00 GMT</pubDate>
      <dc:creator>ACM TOIS (Information Systems)</dc:creator>
      <category>Search and retrieval</category>
      <description>ACM TOIS (Information Systems) — Did: Sequential recommendation methods are important in recommender systems, as they capture users’ dynamic interests from their historical behavior sequences.</description>
    </item>
    <item>
      <title>Distilling Transitional Patterns to Multimodal Large Language Models for Session-based Recommendation</title>
      <link>https://dl.acm.org/doi/10.1145/3856985</link>
      <guid isPermaLink="true">https://dl.acm.org/doi/10.1145/3856985</guid>
      <pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate>
      <dc:creator>ACM TOIS (Information Systems)</dc:creator>
      <category>Search and retrieval</category>
      <description>ACM TOIS (Information Systems) — Did: Session-based recommendation (SBR) predicts the next item based on anonymous sessions.</description>
    </item>
    <item>
      <title>GaCLLM: Large Language Model with Graph Convolution for Recommendation</title>
      <link>https://dl.acm.org/doi/10.1145/3857345</link>
      <guid isPermaLink="true">https://dl.acm.org/doi/10.1145/3857345</guid>
      <pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate>
      <dc:creator>ACM TOIS (Information Systems)</dc:creator>
      <category>Search and retrieval</category>
      <description>ACM TOIS (Information Systems) — Did: Leveraging auxiliary textual data can help with user profiling and item characterization in recommender systems (RSs).</description>
    </item>
  </channel>
</rss>
