Abstract
In today’s personalized business environment, or-ganizations are providing bulk of information regarding their products and services. Recommender system has various accom-plishment on exploiting auxiliary information in matrix factor-ization. To handle data sparsity problem most recommender systems utilized deep learning techniques for in-depth analysis of item content to generate more accurate recommendations. However, these systems still have a research gap on how to handle user reviews effectively. Reviews that were written by users contain a large amount of information that can be utilized for more accurate predictions. This paper proposes a Hybrid Model to address the sparsity problem, convolutional neural network and topic modeling for recommender system, which extract the contextual features of both items and users by utilizing Deep Learning Convolutional Neural Network (CNN) along with Topic Modeling (Lda2vec) technique to generate latent factors of user and item. Topic Modeling is used to capture important topics from side information and deep learning is used to provide contextual information. To demonstrate the effectiveness of the research, an extensive experimental sets were performed on four public datasets (Amazon Instant Video, Kindle store, Health and Personal Care, Automotive). Results demonstrate that the proposed model outperformed the other state of the art approaches.
Highlights
Recommender systems have become the core component of many e-commence organizations which avails it to predict the liking and disliking of users
The proposed model is termed as the Hybrid model of Convolutional Neural Network and Topic Modeling (HCNNTM) for recommender system which handles data sparsity problem, model unites CNN + Lda2vec into Probabilistic matrix factorization (PMF) to achieve latent factors of both the user and item enriched with topic information
The major contribution of this study suggested a Hybrid content Embedding Model for Recommender System, HCNNTM, which combines topic modeling with deep learning techniques to provide topic enriched contextual features of both user and item which will improve accuracy of rating prediction
Summary
Recommender systems have become the core component of many e-commence organizations (i.e. movies web sites, elibraries, articles, news, music, etc.) which avails it to predict the liking and disliking of users. There exists a research gap of data sparsity in recommender system which need improvements and more accurate recommendations [11], [12] To deal with this data sparsity problem in RS, a hybrid model is proposed which fuses rating data along with the information (reviews) of both user and item. The proposed model is termed as the Hybrid model of Convolutional Neural Network and Topic Modeling (HCNNTM) for recommender system which handles data sparsity problem, model unites CNN + Lda2vec into PMF to achieve latent factors of both the user and item enriched with topic information. The major contribution of this study suggested a Hybrid content Embedding Model for Recommender System, HCNNTM, which combines topic modeling with deep learning techniques to provide topic enriched contextual features of both user and item which will improve accuracy of rating prediction.
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