Bilgisayar ve Bilişim Fakültesi

Bu topluluk için Kalıcı Uri

http://hdl.handle.net/11527/25353

Gözat

Yazar "Tugay, Resul" ile Bilgisayar ve Bilişim Fakültesi'a göz atma
  • Öge
    A GNN model with adaptive weights for session-based recommendation systems
    (ACM, 2024) Özbay, Begüm ; Tugay, Resul ; Gündüz Öğüdücü, Şule ; Bilgisayar Mühendisliği
    Session-based recommendation systems aim to model users’ interests based on their sequential interactions to predict the next item in an ongoing session. In this work, we present a novel approach that can be used in session-based recommendations (SBRs). Our goal is to enhance the prediction accuracy of an existing session-based recommendation model, the SR-GNN model, by introducing an adaptive weighting mechanism applied to the graph neural network (GNN) vectors. This mechanism is designed to incorporate various types of side information obtained through different methods during the study. Items are assigned varying degrees of importance within each session as a result of the weighting mechanism. We hypothesize that this adaptive weighting strategy will contribute to more accurate predictions and thus improve the overall performance of SBRs in different scenarios. The adaptive weighting strategy can be utilized to address the cold start problem in SBRs by dynamically adjusting the importance of items in each session, thus providing better recommendations in cold start situations, such as for new users or newly added items. Our experimental evaluations on the Dressipi dataset demonstrate the effectiveness of the proposed approach compared to traditional models in enhancing the user experience and highlighting its potential to optimize the recommendation results in real-world applications.
  • Öge
    Enhancing cross-market recommendation system with graph isomorphism networks: a novel approach to personalized user experience
    (ACM, 2024) Öztürk, Sümeyye ; Ercan, Ahmed Burak ; Tugay, Resul ; Öğüdücü, Şule ; https://orcid.org/0009-0002-1308-7646 ; https://orcid.org/0009-0001-4051-8424 ; https://orcid.org/0000-0003-1621-9528 ; https://orcid.org/0000-0002-0288-4757 ; Yapay Zeka ve Veri Mühendisliği
    In today’s world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algorithms have difficulties dealing with market specificity and data sparsity, especially in new or emerging markets. In this paper, we propose the CrossGR model, which utilizes Graph Isomorphism Networks (GINs) to improve CMR systems. It outperforms existing benchmarks in NDCG@10 and HR@10 metrics, demonstrating its adaptability and accuracy in handling diverse market segments. The CrossGR model is adaptable and accurate, making it well-suited for handling the complexities of cross-market recommendation tasks. Its robustness is demonstrated by consistent performance across different evaluation timeframes, indicating its potential to cater to evolving market trends and user preferences. Our findings suggest that GINs represent a promising direction for CMRs, paving the way for more sophisticated, personalized, and context-aware recommendation systems in the dynamic landscape of global e-commerce.