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Researcher profile

Alexandros Karatzoglou

9 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. The Contextual Turn

    2016

    A critical change has occurred in the status of context in recommender systems. In the past, context has been considered 'additional evidence'. This past picture is at odds with many present application domains, where user …

  2. A Simple Convolutional Generative Network for Next Item Recommendation

    2019

    Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into …

  3. Self-Supervised Reinforcement Learning for Recommender Systems

    2020 · arXiv (Cornell University)

    In session-based or sequential recommendation, it is important to consider a number of factors like long-term user engagement, multiple types of user-item interactions such as clicks, purchases etc. The current state-of-the-art supervised approaches fail to …

  4. Choosing the Best of Both Worlds

    2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

    Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms. However, by focusing on item relevance, one …

  5. Session-based Recommendations with Recurrent Neural Networks

    2015 · arXiv (Cornell University)

    We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) …

  6. Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

    2016

    Real-life recommender systems often face the daunting task of providing recommendations based only on the clicks of a user session. Methods that rely on user profiles -- such as matrix factorization -- perform very poorly …

  7. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

    2017

    Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based …

  8. Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

    2018

    RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical …

  9. Collaborative Filtering Bandits

    2016

    Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, …