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Showing 1 to 5 of 5 for “"Personalized Search"”.

  1. User-Centered Adaptive Information Retrieval

    … center of information retrieval process for the personalized search. We develop a decision-theoretic framework for optimizing interactive information retrieval based on eager user model updating. The framework emphasizes immediate and frequent feedback to bring maximum benefit of context to the …

    uiuc Repository record for User-Centered Adaptive Information Retrieval (opens in a new tab)

  2. Supporting finding and re-finding through personalization

    … one of the most common uses for the Internet to search for information, Web search tools often fail to connect people with what they are looking for. This is because search tools are designed to satisfy people in general, not the searcher in particular. Different individuals with different …

    mit Repository record for Supporting finding and re-finding through personalization (opens in a new tab)

  3. A study of language models for exploiting user feedback in Information Retrieval

    … users provide contextual information about their search needs, with the goal of improving retrieval accuracy and achieving personalization. Relevance feedback has been studied extensively, and in recent years new types of feedback such as implicit feedback and collective feedback have attracted …

    uiuc Repository record for A study of language models for exploiting user feedback in Information Retrieval (opens in a new tab)

  4. Participating and designing around algorithmic socio-technical systems

    … such as social media feeds, recommendations and personalized search results. These algorithms curate everyday online content by prioritizing, classifying, associating, and filtering information. However, while these algorithms have great power to shape users’ experiences, users are often unaware …

    uiuc Repository record for Participating and designing around algorithmic socio-technical systems (opens in a new tab)

  5. Neural recommender models for sparse and skewed behavioral data

    Modern online platforms offer recommendations and personalized search and services to a large and diverse user base while still aiming to acquaint users with the broader community on the platform. Prior work backed by large volumes of user data has shown that user retention is reliant on catering …

    uiuc Repository record for Neural recommender models for sparse and skewed behavioral data (opens in a new tab)