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.
Original language | English |
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Title of host publication | New Frontiers in Mining Complex Patterns - 8th International Workshop, NFMCP 2019, held in Conjunction with ECML-PKDD 2019, Revised Selected Papers |
Editors | Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras |
Publisher | Springer |
Pages | 52-66 |
Number of pages | 15 |
ISBN (Print) | 9783030488604 |
DOIs | |
Publication status | Published - 2020 |
Event | 8th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2019, held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2019 - Würzburg, Germany Duration: 16 Sept 2019 → 16 Sept 2019 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11948 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 8th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2019, held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2019 |
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Country/Territory | Germany |
City | Würzburg |
Period | 16/09/19 → 16/09/19 |
Bibliographical note
Publisher Copyright:© Springer Nature Switzerland AG 2020.
Keywords
- Hybrid recommenders
- Incomplete data
- Learning latent representation
- Neural networks
- Personalization
- Recommender systems