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Filtering by: Creator Riedl, Helmut W. Remove constraint Creator: Riedl, Helmut W. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • A collaborative filtering algorithm and evaluation metric that accurately model the user experience

    Creator:
    McLaughlin, Matthew
    Abstract:
    "Collaborative filtering algorithms’ performances have been evaluated using a variety of metrics. These metrics, such as Mean Absolute Error and Precision, have often ignored recommendations for which they do not have data. Ignoring these recommendations has provided numbers which do not accurately represent the user experience. Qualitatively we have seen...
    Resource Type:
    Capstone Project
    Full Text:
    user u, let w(a,u) be the Pearson-Correlation between user a and user u. After computing the

  • Designing and understanding information retrieval systems using collaborative filtering in an academic library environment

    Creator:
    Jung, Seikyung
    Abstract:
    Accessing information on the Web has become ingrained into our daily lives, and we seek information from many different sources, including conference and journal publications, personal web pages, and others. Increasingly, web-based information retrieval systems such as web-based search engines, library on-line catalog systems, and subscription-based federated search systems are...
    Resource Type:
    Dissertation

  • Applying machine learning for prediction, recommendation, and integration

    Creator:
    Bao, Xinlong
    Abstract:
    This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating Learning and Reasoning. FolderPredictor is an intelligent desktop software tool that helps...
    Resource Type:
    Dissertation

  • Object Tracking-by-Segmentation in Videos

    Creator:
    Chen, Sheng
    Abstract:
    This thesis focuses on the problem of object tracking. Given a video, the general objective of tracking is to track the location over time of one or more targets in the image sequence. This is a very challenging task as algorithms need to deal with problems such as appearance variations,...
    Resource Type:
    Dissertation
    Full Text:
    set, the algorithm performs one run of learning the weight vector w. . . 15 2 Inequality constraint

  • Improving automated email tagging with implicit feedback

    Creator:
    Sorower, Mohammad Shahed
    Abstract:
    Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
    Resource Type:
    Dissertation

  • Deep Learning for Human and Biological Languages

    Creator:
    Hong, Junki
    Abstract:
    We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
    Resource Type:
    Dissertation
    Full Text:
    . Stack w/o Bookkeeping . . . . . . . . . . . . . . . . . . 7 2.3.3 Action-Synchronous Beam Search

  • Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

    Creator:
    Dodge, Jonathan E.
    Abstract:
    Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
    Resource Type:
    Dissertation
    Full Text:
    nearest half-hour, and records are presented as [W, L D]. Tables G.1–G.4 all will use a similar format