Extracting the Information Backbone in Online System

Zhang, Qian-Ming; Zeng, An; Shang, Mingsheng

doi:10.1371/journal.pone.0062624

Cited by 54 publications

(46 citation statements)

References 22 publications

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“…The main task of recommender system is to predict future links of each target user based on historical rating records [31,32,[35][36][37], in which the whole data set is divided into a training set and a test set and only the links in training set is known before recommendation. However, in most of the previous studies, as far as we know, the test data set is sampled randomly without consider the temporal order of the links, leading to a logical disorder in the recommendation process, i.e., predicting the past links in test set on the basis of the future links in training set.…”

Section: Related Workmentioning

confidence: 99%

Information filtering in evolving online networks

Chen

Zhang

et al. 2018

Physics Letters A

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Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics.

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Section: Related Workmentioning

confidence: 99%

Information filtering in evolving online networks

Chen

Zhang

et al. 2018

Physics Letters A

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“…Motivated by the observed significant difference between users' structural properties in the network, Zhang et al proposed to remove redundant links for each user to extract the so-called information backbone [19]. Guan et al observed that large degree users tend to select niche objects while small degree users tend to select popular objects.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, two more recent algorithms are compared. The first one is called Most Popular Removal algorithm (MPR) which recommend items based on the information backbone of the user-item network [19]. The popularity of a link ia is defined as…”

Section: Tablementioning

confidence: 99%

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The effect of heterogeneous dynamics of online users on information filtering

2015

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The rapid expansion of the Internet requires effective information filtering techniques to extract the most essential and relevant information for online users. Many recommendation algorithms have been proposed to predict the future items that a given user might be interested in. However, there is an important issue that has always been ignored so far in related works, namely the heterogeneous dynamics of online users. The interest of active users changes more often than that of less active users, which asks for different update frequency of their recommendation lists. In this paper, we develop a framework to study the effect of heterogeneous dynamics of users on the recommendation performance. We find that the personalized application of recommendation algorithms results in remarkable improvement in the recommendation accuracy and diversity. Our findings may help online retailers make better use of the existing recommendation methods.

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“…On the other hand, there is a problem of information overload. The large amount of data generated every day makes it difficult for items we want to be chosen as easily as previously [2]. Personalized recommendation is considered the most effective way to solve the problem of information overload [3,4] and thus far, recommendation systems have been used in many fields [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Triangular Personalized Recommendation Algorithm for Improving Diversity

Cai

Yang

Huang

et al. 2018

Discrete Dynamics in Nature and Society

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Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a "third dimension" to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods.

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Extracting the Information Backbone in Online System

Cited by 54 publications

References 22 publications

Information filtering in evolving online networks

Information filtering in evolving online networks

The effect of heterogeneous dynamics of online users on information filtering

A Triangular Personalized Recommendation Algorithm for Improving Diversity

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