Neural Hybrid Recommender: Recommendation needs collaboration
This work addresses the need for more flexible and effective recommender systems, but it appears incremental as it builds upon existing neural network approaches in this domain.
The authors tackled the problem of improving recommender systems by introducing a generalized neural network-based framework called NHR, which allows for the integration of elaborate information from various data sources, and demonstrated its superior performance over state-of-the-art methods on benchmark and new datasets.
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.