With the advent of the era of big data, the recommendation system has become an effective solution to the problem of information overload. This paper takes the literature data related to the recommendation system theme from 2009 to 2018 and included in the core collection of Web of Science database as the research object, and utilizes bibliometric methods to analyze the theme of recommendation system. To this end, firstly, classify statistics and feature analysis of valid literature data. Secondly, use VOSviewer software to construct various different scientific knowledge graph to discover valuable knowledge. Thirdly, according to keyword co-concurrence graph conclude five main hotspots of current research about recommendation system and discover five main directions that have potential value in research field of recommendation system. Finally, deeply explore five main key issues and propose corresponding solutions.

A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph

Deep learning techniques for recommender systems based on collaborative filtering

In e-commerce websites and related micro-blogs, users supply online reviews expressing their preferences regarding various items. Such reviews are typically in the textual comments form, and account for a valuable information source about user interests. Recently, several works have used review texts and their related rich information like review words, review topics and review sentiments, for improving the rating-based collaborative filtering recommender systems. These works vary from one another on how they exploit the review texts for deriving user interests. This paper provides a detailed survey of recent works that integrate review texts and also discusses how these review texts are exploited for addressing some main issues of standard collaborative filtering algorithms.

Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey

Today, recommender systems play a vital role in the acceleration of searches by internet users to find what they are interested in. Among the strategies proposed for recommender systems, collaborative filtering has received due attention regarding its simplicity and efficiency. The key factor for the success of this strategy returns to the similarity calculation methods that affect the accuracy of its recommendations. Regarding the large volume of articles published in the field of collaborative filtering for the development of recommender systems, it is necessary to provide a comprehensive review of the similarity functions and their efficiency presented in the field. Of course, several surveys have already been published to investigate the similarity functions proposed for collaborative filtering, but these articles either have looked briefly at these functions or reviewed a few numbers of them. In this study, the effort was to provide a comprehensive study on the similarity functions proposed for collaborative filtering with a special focus on the rating-based and neighbor-based approaches. After a brief explanation of each similarity function, some popular evaluation metrics were used for their evaluation using the MovieLens datasets. The comparative evaluation results presented in this article provide a highly useful reference for researchers in this field to choose their appropriate similarity function.

Survey of similarity functions on neighborhood-based collaborative filtering

The recommendation system is an important and widely used technology in the era of Big Data. Current methods have fused side information into it to alleviate the sparsity problem, one of the key problems of recommendation systems. However, not all the side information can be obtained with high quality, and the specific methods based on side information are not universal. In addition, side information has not been mined by the existing graph-based methods. To address these problems, we propose a Dynamic evolution of Multi-Graph Collaborative Filtering (DMGCF) model to mine and reuse side information. Specifically, we first construct user graph and item graph based on user-item bipartite graph and embeddings to exploit inter-user and inter-item relationships. The two new graphs simulate side information in latent space. Next, we perform a dual-path graph convolution network (GCN) on these three graphs for collaborative filtering. Then, a novel dynamic evolution mechanism is proposed to update and promote the embeddings and graphs collaboratively during the learning process, which produces better embeddings, user and item relationships, as well as the rating scores. We conduct a series of experiments on real-world datasets, and experimental results show the effectiveness of our approach.

Dynamic evolution of multi-graph based collaborative filtering for recommendation systems

In the era of big data, recommender system (RS) has become an effective information filtering tool that alleviates information overload for Web users. Collaborative filtering (CF), as one of the most successful recommendation techniques, has been widely studied by various research institutions and industries and has been applied in practice. CF makes recommendations for the current active user using lots of users’ historical rating information without analyzing the content of the information resource. However, in recent years, data sparsity and high dimensionality brought by big data have negatively affected the efficiency of the traditional CF-based recommendation approaches. In CF, the context information, such as time information and trust relationships among the friends, is introduced into RS to construct a training model to further improve the recommendation accuracy and user’s satisfaction, and therefore, a variety of hybrid CF-based recommendation algorithms have emerged. In this paper, we mainly review and summarize the traditional CF-based approaches and techniques used in RS and study some recent hybrid CF-based recommendation approaches and techniques, including the latest hybrid memory-based and model-based CF recommendation algorithms. Finally, we discuss the potential impact that may improve the RS and future direction. In this paper, we aim at introducing the recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.

A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks

In traditional inpainting method for object removal, the SSD (Sum of Squared Differences) is always used to measure the degree of similarity between exemplar patch and target patch. Although the matching rule is simple, there is a risk that the target patch is replaced by an unsuitable exemplar patch, which leads to the mismatch error. Even worse, the error may be constantly accumulated along with the process progresses, finally some unexpected objects will be introduced into target region, and the restored image cannot meet the requirements of human vision. In view of these problems, we propose an inpainting method based on adaptive two-round search strategy. Firstly, we define the DBP (Differences Between Patches) between target patch and exemplar patch, and use it to measure the degree of difference between the two patches. Then, based on SSD and DBP, we adaptively judge whether there is a mismatch error. If the mismatch error occurs, the two-round search strategy is implemented. We define a new matching rule and use it to re-search the exemplar patch. Finally, we use the exemplar patch to restore the target patch. Experimental results demonstrate the effectiveness of our method. It can effectively prevent the occurrence of mismatch error and error accumulation, improve the restoration effect.

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Lei Zhang

Papers

A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks

Image Inpainting for Object Removal Based on Adaptive Two-Round Search Strategy

Image inpainting based on sparse representation using self-similar joint sparse coding