Multi-model deep learning approach for collaborative filtering recommendation system

AbstractThe collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.

Download Full-text

Adaptable Recommendation System for Outfit Selection with Deep Learning Approach

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2021.10.516 ◽

2021 ◽

Vol 54 (13) ◽

pp. 605-610

Author(s):

Laura J. Padilla Reyes ◽

Natalia Bonifaz Oviedo ◽

Edgar C. Camacho ◽

Juan M. Calderon

Keyword(s):

Deep Learning ◽

Recommendation System ◽

Learning Approach

Download Full-text

Domain Specific Publication Recommendation System using Deep Learning Approach

SSRN Electronic Journal ◽

10.2139/ssrn.3444041 ◽

2019 ◽

Author(s):

Muhsina V P ◽

Naseer C

Keyword(s):

Deep Learning ◽

Recommendation System ◽

Learning Approach ◽

Domain Specific ◽

Publication Recommendation

Download Full-text

Using deep learning approach and IoT architecture to build the intelligent music recommendation system

Soft Computing ◽

10.1007/s00500-020-05364-y ◽

2020 ◽

Author(s):

Xinglin Wen

Keyword(s):

Deep Learning ◽

Recommendation System ◽

Learning Approach ◽

Music Recommendation ◽

Music Recommendation System

Download Full-text

Deep learning approach to obtain collaborative filtering neighborhoods

Neural Computing and Applications ◽

10.1007/s00521-021-06493-7 ◽

2021 ◽

Author(s):

Jesús Bobadilla ◽

Ángel González-Prieto ◽

Fernando Ortega ◽

Raúl Lara-Cabrera

Keyword(s):

Neural Network ◽

Deep Learning ◽

Collaborative Filtering ◽

Recommender Systems ◽

Nearest Neighbors ◽

Learning Approach ◽

Localization Algorithm ◽

Generative Process ◽

Open Datasets

AbstractIn the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding differentiating factors to recommendations, such as explainability, detecting shilling attacks, visualizing item relations, clustering, and providing reliabilities. This paper proposes a deep learning architecture to efficiently and accurately obtain CF neighborhoods. The proposed design makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm. Experiments have been conducted using five popular open datasets and five representative baselines. The results show that the proposed method improves the quality of the neighborhoods compared to the K-Nearest Neighbors (KNN) algorithm for the five selected similarity measure baselines. The efficiency of the proposed method is also shown by comparing its computational requirements with that of KNN.

Download Full-text