Improving diffusion-based recommendation in online rating systems

Network diffusion processes play an important role in solving the information overload problem. It has been shown that the diffusion-based recommendation methods have the advantage to generate both accurate and diverse recommendation items for online users. Despite that, numerous existing works consider the rating information as link weight or threshold to retain the useful links, few studies use the rating information to evaluate the recommendation results. In this paper, we measure the average rating of the recommended products, finding that diffusion-based recommendation methods have the risk of recommending low-rated products to users. In addition, we use the rating information to improve the network-based recommendation algorithms. The idea is to aggregate the diffusion results on multiple user-item bipartite networks each of which contains only links of certain ratings. By tuning the parameters, we find that the new method can sacrifice slightly the recommendation accuracy for improving the average rating of the recommended products.

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A Triangular Personalized Recommendation Algorithm for Improving Diversity

Discrete Dynamics in Nature and Society ◽

10.1155/2018/3162068 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Biao Cai ◽

Xiaowang Yang ◽

Yusheng Huang ◽

Hongjun Li ◽

Qiang Sang

Keyword(s):

Collaborative Filtering ◽

Information Overload ◽

Personalized Recommendation ◽

Recommendation Algorithm ◽

Filtering Method ◽

Online Databases ◽

Recommendation Algorithms ◽

Recommendation Accuracy ◽

Third Dimension ◽

Triangular Area

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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A Novel Preferential Diffusion Recommendation Algorithm Based on User’s Nearest Neighbors

International Journal of Digital Multimedia Broadcasting ◽

10.1155/2017/1386461 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7

Author(s):

Fuguo Zhang ◽

Yehuan Liu ◽

Qinqiao Xiong

Keyword(s):

Information Overload ◽

Nearest Neighbors ◽

Data Sets ◽

The Novel ◽

Real World Data ◽

Recommendation Algorithm ◽

Preferential Diffusion ◽

Recommendation Algorithms ◽

Recommendation Accuracy ◽

High Degree

Recommender system is a very efficient way to deal with the problem of information overload for online users. In recent years, network based recommendation algorithms have demonstrated much better performance than the standard collaborative filtering methods. However, most of network based algorithms do not give a high enough weight to the influence of the target user’s nearest neighbors in the resource diffusion process, while a user or an object with high degree will obtain larger influence in the standard mass diffusion algorithm. In this paper, we propose a novel preferential diffusion recommendation algorithm considering the significance of the target user’s nearest neighbors and evaluate it in the three real-world data sets: MovieLens 100k, MovieLens 1M, and Epinions. Experiments results demonstrate that the novel preferential diffusion recommendation algorithm based on user’s nearest neighbors can significantly improve the recommendation accuracy and diversity.

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A personalized network-based recommendation approach via distinguishing user’s preference

International Journal of Modern Physics B ◽

10.1142/s0217979219500292 ◽

2019 ◽

Vol 33 (06) ◽

pp. 1950029 ◽

Cited By ~ 1

Author(s):

Wei Wu ◽

Ruoxi Zhang ◽

Lianggui Liu

Keyword(s):

Rapid Growth ◽

Information Overload ◽

Internet Technology ◽

Intelligence Analysis ◽

Rating Systems ◽

Resource Configuration ◽

Market Intelligence ◽

Recommendation Algorithms ◽

Novel Method ◽

The Impact

With the rapid growth of commerce and development of Internet technology, a large number of user consumption preferences become available for online market intelligence analysis. A critical demand is to reduce the impact of information overload by using recommendation algorithms. In physical dynamics, network-based recommendation algorithms based on mass-diffusion have been popular for its simplicity and efficiency. In this paper, to solve the problem that most network-based recommendation algorithms cannot distinguish how much the user likes collected items and make resource configuration more reasonable, we propose a novel method called biased network-based inference (BNBI). The proposed method treats rating systems and nonrating systems differently and measures user’s preference for items by means of item similarity. The proposed method is evaluated in real datasets (MovieLens and Last.FM) and compared with some existing classic recommendation algorithms. Experimental results show that the proposed method is more effective and it can reduce the impact of item diversity and discover the real interest of users.

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Evaluating user reputation of online rating systems by rating statistical patterns

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.106895 ◽

2021 ◽

pp. 106895

Author(s):

Hong-Liang Sun ◽

Kai-Ping Liang ◽

Hao Liao ◽

Duan-Bing Chen

Keyword(s):

Rating Systems ◽

Online Rating ◽

User Reputation

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Dose the Weight Matter? An Empirical Study of Online Rating Systems in Buy-It-Now C2C Exchanges

Communications in Computer and Information Science - Advances in Applied Economics, Business and Development ◽

10.1007/978-3-642-23020-2_45 ◽

2011 ◽

pp. 300-306

Author(s):

Xianfeng Zhang ◽

Nan Zhang

Keyword(s):

Empirical Study ◽

Rating Systems ◽

Online Rating

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Empirical analysis of the user reputation and clustering property for user-object bipartite networks

International Journal of Modern Physics C ◽

10.1142/s0129183119500359 ◽

2019 ◽

Vol 30 (05) ◽

pp. 1950035 ◽

Cited By ~ 1

Author(s):

Xiao-Lu Liu ◽

Shu-Wei Jia ◽

Yan Gu

Keyword(s):

Standard Deviation ◽

Null Model ◽

Clustering Coefficient ◽

Behavior Patterns ◽

Bipartite Networks ◽

Rating Systems ◽

User Interest ◽

Collective Behaviors ◽

User Reputation ◽

Clustering Property

User reputation is of great significance for online rating systems which can be described by user-object bipartite networks, measuring the user ability of rating accurate assessments of various objects. The clustering coefficients have been widely investigated to analyze the local structural properties of complex networks, analyzing the diversity of user interest. In this paper, we empirically analyze the relation of user reputation and clustering property for the user-object bipartite networks. Grouping by user reputation, the results for the MovieLens dataset show that both the average clustering coefficient and the standard deviation of clustering coefficient decrease with the user reputation, which are different from the results that the average clustering coefficient and the standard deviation of clustering coefficient remain stable regardless of user reputation in the null model, suggesting that the user interest tends to be multiple and the diversity of the user interests is centralized for users with high reputation. Furthermore, we divide users into seven groups according to the user degree and investigate the heterogeneity of rating behavior patterns. The results show that the relation of user reputation and clustering coefficient is obvious for small degree users and weak for large degree users, reflecting an important connection between user degree and collective rating behavior patterns. This work provides a further understanding on the intrinsic association between user collective behaviors and user reputation.

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Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2016040102 ◽

2016 ◽

Vol 8 (2) ◽

pp. 16-26 ◽

Cited By ~ 1

Author(s):

Zhihai Yang ◽

Zhongmin Cai

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Graph Mining ◽

Scale Up ◽

Detection Methods ◽

Detection Accuracy ◽

Rating Systems ◽

Rating Data ◽

Online Rating ◽

Two Stages

Online rating data is ubiquitous on existing popular E-commerce websites such as Amazon, Yelp etc., which influences deeply the following customer choices about products used by E-businessman. Collaborative filtering recommender systems (CFRSs) play crucial role in rating systems. Since CFRSs are highly vulnerable to “shilling” attacks, it is common occurrence that attackers contaminate the rating systems with malicious rates to achieve their attack intentions. Despite detection methods based on such attacks have received much attention, the problem of detection accuracy remains largely unsolved. Moreover, few can scale up to handle large networks. This paper proposes a fast and effective detection method which combines two stages to find out abnormal users. Firstly, the manuscript employs a graph mining method to spot automatically suspicious nodes in a constructed graph with millions of nodes. And then, this manuscript continue to determine abnormal users by exploiting suspected target items based on the result of first stage. Experiments evaluate the effectiveness of the method.

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