Neural Hybrid Recommender: Recommendation needs collaboration

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ID: 283449
2019
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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.
Reference Key
Öğüdücü2019neural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ezgi Yıldırım; Payam Azad; Şule Gündüz Öğüdücü
Journal arXiv
Year 2019
DOI
DOI not found
URL
http://arxiv.org/abs/1909.13330v1
Keywords
cs.lg cs.ir

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