A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering
Due to the widespread availability of implicit feedback (e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback, implicit feedback cannot directly reflect user preferences. Therefore, although more cha...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2018-09-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020019 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832572961806090240 |
|---|---|
| author | Yu Liu Shuai Wang M. Shahrukh Khan Jieyu He |
| author_facet | Yu Liu Shuai Wang M. Shahrukh Khan Jieyu He |
| author_sort | Yu Liu |
| collection | DOAJ |
| description | Due to the widespread availability of implicit feedback (e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback, implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems, some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and content-based recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders (DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods. |
| format | Article |
| id | doaj-art-a53ddef0dab44b9bbbdf6a2515c9a12f |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a53ddef0dab44b9bbbdf6a2515c9a12f2025-02-02T06:00:35ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-09-011321122110.26599/BDMA.2018.9020019A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative FilteringYu Liu0Shuai Wang1M. Shahrukh Khan2Jieyu He3<institution content-type="dept">School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration</institution>, <institution>Southeast University</institution>, <city>Nanjing</city> <postal-code>211189</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration</institution>, <institution>Southeast University</institution>, <city>Nanjing</city> <postal-code>211189</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration</institution>, <institution>Southeast University</institution>, <city>Nanjing</city> <postal-code>211189</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration</institution>, <institution>Southeast University</institution>, <city>Nanjing</city> <postal-code>211189</postal-code>, <country>China</country>.Due to the widespread availability of implicit feedback (e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback, implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems, some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and content-based recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders (DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods.https://www.sciopen.com/article/10.26599/BDMA.2018.9020019hybrid recommender systemneural collaborative filteringauto-encoderimplicit feedback |
| spellingShingle | Yu Liu Shuai Wang M. Shahrukh Khan Jieyu He A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering Big Data Mining and Analytics hybrid recommender system neural collaborative filtering auto-encoder implicit feedback |
| title | A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering |
| title_full | A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering |
| title_fullStr | A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering |
| title_full_unstemmed | A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering |
| title_short | A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering |
| title_sort | novel deep hybrid recommender system based on auto encoder with neural collaborative filtering |
| topic | hybrid recommender system neural collaborative filtering auto-encoder implicit feedback |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020019 |
| work_keys_str_mv | AT yuliu anoveldeephybridrecommendersystembasedonautoencoderwithneuralcollaborativefiltering AT shuaiwang anoveldeephybridrecommendersystembasedonautoencoderwithneuralcollaborativefiltering AT mshahrukhkhan anoveldeephybridrecommendersystembasedonautoencoderwithneuralcollaborativefiltering AT jieyuhe anoveldeephybridrecommendersystembasedonautoencoderwithneuralcollaborativefiltering AT yuliu noveldeephybridrecommendersystembasedonautoencoderwithneuralcollaborativefiltering AT shuaiwang noveldeephybridrecommendersystembasedonautoencoderwithneuralcollaborativefiltering AT mshahrukhkhan noveldeephybridrecommendersystembasedonautoencoderwithneuralcollaborativefiltering AT jieyuhe noveldeephybridrecommendersystembasedonautoencoderwithneuralcollaborativefiltering |