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Auflistung nach Schlagwort "Recommender System"

1 - 5 von 5
  • Konferenzbeitrag
    Comparative Evaluation for Recommender Systems for Book Recommendations
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Tashkandi, Araek; Wiese, Lena; Baum, Marcus
    Recommender System (RS) technology is often used to overcome information overload. Recently, several open-source platforms have been available for the development of RSs. Thus, there is a need to estimate the predictive accuracy of such platforms to select a suitable framework. In this paper we perform an offline comparative evaluation of commonly used recommendation algorithms of collaborative filtering. They are implemented by three popular RS platforms (LensKit, Mahout, and MyMediaLite) using the BookCrossing data set containing 1,149,780 user ratings on books. Our main goal is to find out which of these RSs is the most applicable and has high performance and accuracy on these data. We consider performing a fair objective comparison by benchmarking the evaluation dimensions such as the data set and the evaluation metric. Our evaluation shows the disparity of evaluation results between the RS frameworks. This points to the need of standardizing evaluation methodologies for recommendation algorithms.
  • Konferenzbeitrag
    Design of a Knowledge-Based Recommender System for Recipes from an End-User Perspective
    (Mensch und Computer 2021 - Tagungsband, 2021) Niessner, Julia; Ludwig, Thomas
    Nowadays, recommender systems are a fundamental part of several online services. However, most of these systems rely on collective user data and ratings or a preselection of parameters to derive appropriate recommendations. Within this paper, we examine recommendations without previous user data. We therefore designed and evaluated a knowledge-based recommender system by turning to recipe recommendations that offer alternatives for favorite recipes. We introduce and compare three versions of a given algorithm. Our evaluation shows that the knowledge-based approach may serve as a good start for deriving appropriate recommendations without prior user data. Moreover, we show that end-users’ assumptions about decisive criteria of a recommender system do not necessarily match the later actual decisive criteria.
  • Zeitschriftenartikel
    Experten-Empfehlungen mit Social Bookmarking-Services
    (i-com: Vol. 9, No. 3, 2010) Heck, Tamara; Peters, Isabella
    Empfehlungssysteme haben sich insbesondere im e-Commerce etabliert, aber die Empfehlung von Experten oder Mitarbeitern in einem fi rmeninternen Netzwerk oder in wissenschaftlichen Disziplinen wird derzeit noch theoretisch diskutiert. Wir präsentieren einen Ansatz zur Entwicklung von Expertenempfehlungssystemen, der auf Beziehungen in digitalen sozialen Netzwerken wie Social Bookmarking-Systemen mit ihren Folksonomies beruht.
  • Konferenzbeitrag
    KaggleGPT: Prompt-based Recommender System for Efficient Dataset Discovery
    (Proceedings of DELFI Workshops 2024, 2024) Bhoyar, Rahul Rajkumar; Wang, Xia; Duong-Trung, Nghia
    Searching appropriate experimental datasets for machine learning projects and reducing the need for one-on-one student-teacher consultations are both challenging. Despite over 50,000 different datasets available across multiple domains on websites like Kaggle, practitioners often need help locating the necessary datasets. Even with the aid of Kaggle’s API and web search functionalities, the search results are not organized meaningfully to a specific context. Recent developments in artificial intelligence (AI) and large language models (LLMs) provide new means of addressing these relevant issues, which were impossible before. This paper introduces KaggleGPT, an LLM- assisted conversational recommender system designed to streamline finding suitable datasets for students’ projects directly from the textual content. The core of KaggleGPT employs a comprehensive approach by integrating profile-based, expert-based, knowledge-based, and multi-criteria-based recommendation engines. Our vision is for educators and students using KaggleGPT to enhance the educational experience and make dataset discovery more efficient and user-friendly.
  • Workshopbeitrag
    Personalised Training: Integrating Recommender Systems in XR Training Platforms
    (Mensch und Computer 2022 - Workshopband, 2022) Pretolesi, Daniele
    The fast-paced growth of Extended Reality (XR) technologies in complex environments, such as training scenarios, has highlighted the need to implement Artificial Intelligence (AI) modules in the simulations to support trainers and trainees in these unfamiliar contexts. Among the possible AI solutions, recommender systems (RS) could be used to improve the users’ interactions and experience in immersive training environments. This work describes the integration of a RS in the framework of an XR training platform and how the design of interfaces to present recommendations can maximize acceptance of the suggestions in hybrid human-intelligent systems. By allowing trainers to adapt training scenarios during the execution of the exercise, successful and personalized training goals can be achieved.