Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems

With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decision support systems by providing personalized suggestions helping users access what they need more efficiently. Among the different techniques for building a recommender system, Collaborative Filtering (CF) is the most popular and widespread approach. However, cold start and data sparsity are the fundamental challenges ahead of implementing an effective CF-based recommender. Recent successful developments in enhancing and implementing deep learning architectures motivated many studies to propose deep learning-based solutions for solving the recommenders' weak points. In this research, unlike the past similar works about using deep learning architectures in recommender systems that covered different techniques generally, we specifically provide a comprehensive review of deep learning-based collaborative filtering recommender systems. This in-depth filtering gives a clear overview of the level of popularity, gaps, and ignored areas on leveraging deep learning techniques to build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Joint Neural Collaborative Filtering for Recommender Systems

We propose a J-NCF method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization, which takes both implicit and explicit feedback, point-wise and pair-wise loss into account. Experiments on several real-word datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24% and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state-of-the-art baselines.Comment: 30 page

Review-based collaborative recommender system using deep learning methods

Recommender systems have been widely adopted to assist users in purchasing and increasing sales. Collaborative filtering techniques have been identified to be the most popular methods used for the recommendation system. One major drawback of these approaches is the data sparsity problem, which generally leads to low performances of the recommender systems. Recent development has shown that user review texts can be exploited to tackle the issue of data sparsity thereby improving the accuracy of the recommender systems. However, the problem with existing methods for the review-based recommender system is the use of handcrafted features which makes the system less accurate. Thus, to address the above issue, this study proposed collaborative recommender system models that utilize user textual reviews based on deep learning methods for improving predictive performances of recommender systems. To extract the product aspects to mine users‟ opinion, an aspect extraction method was first developed using a Multi-Channel Convolutional Neural Network. An aspect-based recommender system was then designed by integrating the opinions of users based on the product aspects into the collaborative filtering method for the recommendation process. To further improve the predictive performance, the fine-grained user-item interaction based on the aspect-based collaborative method was studied and a sentiment-aware recommender system was also designed using a deep learning method. Extensive series of experiments were conducted on real-world datasets from the Semeval-014, Amazon, and Yelp reviews to evaluate the performances of the proposed models from both the aspect extraction and rating prediction. Experimental results showed that the proposed aspect extraction model performed better than compared methods such as rule-based and the neural network-based approaches, with average gains of 5.2%, 12.0%, and 7.5% in terms of Precision, Recall, and F1 score, respectively. Meanwhile, the proposed aspect-based collaborative methods demonstrated better performances compared to benchmark approaches such as topic modelling techniques with an average improvement of 6.5% and 8.0% in terms of the Root Means Squared Error (RMSE) and Mean Absolute Error (MAE), respectively. Statistical T-test was conducted and the results showed that all the performance improvements were significant at P<0.05. This result indicates the effectiveness of utilizing the multi-channel convolutional neural network for better extraction accuracy. The findings also demonstrate the advantage of utilizing user textual reviews and the deep learning methods for improving the predictive accuracy in recommendation systems