Abstract
In recent years, deep learning has gained an indisputable success in computer vision, speech recognition, and natural language processing. After its rising success on these challenging areas, it has been studied on recommender systems as well, but mostly to include content features into traditional methods. In this paper, we introduce a generalized neural network-based recommender framework that is easily extendable by additional networks. This framework named NHR, short for Neural Hybrid Recommender allows us to include more elaborate information from the same and different data sources. We have worked on item prediction problems, but the framework can be used for rating prediction problems as well with a single change on the loss function. To evaluate the effect of such a framework, we have tested our approach on benchmark and not yet experimented datasets. The results in these real-world datasets show the superior performance of our approach in comparison with the state-of-the-art methods.
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Acknowledgements
This study is part of the research project supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) (Project No: 5170032). This work was also supported by the Research Fund of the Istanbul Technical University (Project Number: BAP-40737). We would like to thank Kariyer.Net for providing us with the online recruiting dataset used in the paper.
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Yıldırım, E., Azad, P., Öğüdücü, Ş.G. (2020). Neural Hybrid Recommender: Recommendation Needs Collaboration. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2019. Lecture Notes in Computer Science(), vol 11948. Springer, Cham. https://doi.org/10.1007/978-3-030-48861-1_4
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