Hybrid Collaborative Filtering Based on Users Rating Behavior

Ortega, Fernando; Rojo, Diego; Valdiviezo, Priscila; Raya, Laura

doi:10.1109/access.2018.2881074

Cited by 18 publications

(7 citation statements)

References 34 publications

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“…Compared with manually extracted features, the advantage of rating data is that it is straightforward to get from users, which does not require prior knowledge. Various kinds of collaborative filtering approaches ( [10], [11], [13], [18], [19], [21], [30] etc.) have been developed to improve the quality of the recommendations based on rating data.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Deep Learning Model to Infer User Demographic Information From Ratings

Liu

Chen

et al. 2019

IEEE Access

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Obtaining demographics of online users is of great significance to Internet service providers and advertisers. Most previous works used standard machine learning methods to infer user demographics from handcrafted features. This has two disadvantages. First, the handcrafted features are usually not robust and rely too much on expert experience. Second, these low-capacity models can neither model the complex nonlinear relationship between users nor recognize interdependencies among items. To address these problems, we propose a DEep REtentive learning frameworK (DEREK) for demographic information prediction. Specifically, we introduce a heuristic data generation method that can alleviate data sparsity in order to use rating data more efficiently than the handcrafted features. Moreover, the retention blocks based on high-capacity deep neural networks are designed to extract a share representation from input rating data. Finally, the DEREK can simultaneously infer different demographic attributes through end-to-end multilabel learning architecture. The extensive experimental results on Movielens 100-k and Movielens 1-M data sets have demonstrated the superiority of the proposed DEREK compared with standard machine learning methods (logistic regression and SVM). INDEX TERMS User demographics, rating data, feature extractor, multi-task multi-class prediction, deep learning.

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Section: Related Workmentioning

confidence: 99%

An Efficient Deep Learning Model to Infer User Demographic Information From Ratings

Liu

Chen

et al. 2019

IEEE Access

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“…However, current collaborative filtering recommendation algorithms face a series of significant problems due to their own algorithmic characteristics, such as difficulties in ensuring real-time algorithm performance when dealing with huge data volumes. Ortega et al developed a hybrid recommendation algorithm with multiclass classification algorithms and executed based on user rating behavior to improve the prediction and recommendation quality [3]. In addition, the artificial bee colony algorithm (ABC) has been effectively used in improving clustering performance due to its fewer parameters, simplicity, and ease of implementation and global merit seeking capability [4].…”

Section: Introductionmentioning

confidence: 99%

Application of Improved K-Means Algorithm in Collaborative Recommendation System

Jing

2022

Journal of Applied Mathematics

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With the explosive growth of information resources in the age of big data, mankind has gradually fallen into a serious “information overload” situation. In the face of massive data, collaborative filtering algorithm plays an important role in information filtering and information refinement. However, the recommendation quality and efficiency of collaborative filtering recommendation algorithms are low. The research combines the improved artificial bee colony algorithm with K-means algorithm and applies them to the recommendation system to form a collaborative filtering recommendation algorithm. The experimental results show that the MAE value of the new fitness function is 0.767 on average, which has good separation and compactness in clustering effect. It shows high search accuracy and speed. Compared with the original collaborative filtering algorithm, the average absolute error value of this algorithm is low, and the running time is only 50 s. It improves the recommendation quality and ensures the recommendation efficiency, providing a new research path for the improvement of collaborative filtering recommendation algorithm.

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“…In intelligent recommender systems, user behavior such as clicking, tagging or writing reviews is a direct expression of user preferences. Recommendation technology predicts a user's future behavior by modeling the user's past behavior [1]. However, when a user has little or no past behavior, it causes a cold-start problem [2].…”

Section: Introductionmentioning

confidence: 99%

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

et al. 2020

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Predicting new user behavior has always been a challenging issue in intelligent recommender systems. This challenge is mainly due to the extreme asymmetry of information between new users and old users. Existing factorization models can efficiently process and map asymmetric information, but they are not good at mining deep relationships between contexts when compressing high-dimensional data. In contrast, neural network methods can deeply exploit the relationship between contexts; however, their training cost is much larger than factorization approaches. Therefore, this paper proposes a scalable and efficient recommender to solve the new user problem by filling the gap between factorization models and neural networks. The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts. The efficient part is a factorization model based on a correlation constraint mechanism and a projection strategy, which enables an asymmetric mapping of information between old and new users. The entire recommender fuses the two parts so that the factorization model and the neural network can complement each other. The experimental results show that our approach can achieve a good balance between performance and training efficiency compared to state-of-the-art methods. INDEX TERMS Recommender systems, new user problems, user behavior, context-aware recommendation, efficient training.

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Hybrid Collaborative Filtering Based on Users Rating Behavior

Cited by 18 publications

References 34 publications

An Efficient Deep Learning Model to Infer User Demographic Information From Ratings

An Efficient Deep Learning Model to Infer User Demographic Information From Ratings

Application of Improved K-Means Algorithm in Collaborative Recommendation System

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

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