Collaborative filtering and deep learning based recommendation system for cold start items

By Jian Wei, Jianhua He, Kai Chen, Yi Zhou, Zuoyin Tang. Expert systems with applications, 2017

696 citations recorded by the source corpus.

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/975f29dfa2d4cccae03851b73bda9624b34a9475

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Xiang Wang
2. Xiangnan He
3. Meng Wang
4. Liqiang Nie
5. Aixin Sun
6. Le Wu
7. Zhaochun Ren
8. Tat-seng Chua

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Xiang Wang
3. Liqiang Nie
4. Meng Wang
5. Yi Tay
6. Le Wu
7. Kun Zhang
8. Tat-seng Chua

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Liqiang Nie
5. Le Wu
6. Yi Tay
7. Tat-seng Chua
8. Kun Zhang

Citations in the corpus

• Deep Learning Based Recommender System (2017, methodology): “Autoencoder [34, 88, 89, 114, 116, 126, 137, 138, 141, 160, 178, 188, 208], [4, 10, 32, 94, 151, 152, 159, 171, 172, 189, 197, 209, 210]”
• LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation (2020, methodology): “Wei et al. [38] proposed a framework which tightly coupled CF approach and DNN.”
• Deep Learning and Medical Diagnosis: A Review of Literature (2018, positive): “A large number of newer studies have highlighted the capabilities of advanced deep learning technologies, including learning from complex data [5,6], image recognition [7], text categorization [8] and others.”
• A systematic review and research perspective on recommender systems (2022, methodology): “SDAE [41] is used for learning the non-linear transformations with different filters for finding suitable data.”
• MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation (2019, background): “Recently, to improve performance, the recommendations have been made using deep learning methods [10, 24, 28]; however, the cold-start problem remains for new users who rate only a few items.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “the bottleneck hidden content representations of items [30], [54], [71], [72], [73], [74], [75], [76], [77], [78].”