Study on Directed Trust Graph Based Recommendation for E-commerce System

Abstract

Automated recommender systems have played a more and more important role in marketing and ever increasingly booming e-commerce systems. They provide useful predictions personalized recommendations according to customers characteristics and a variety of large and complex product offerings. In many of these recommendation technologies Collaborative Filtering (CF) has proven to be one of the most successful recommendation method, which has been widely used in many e-commerce systems. The success of CF recommendation depends mainly on locating similar neighbors to get recommendation items. However, many scholars have found that the process of finding similar neighbors often fail, due to some inherent weaknesses of CF based recommendation. In view of this, we propose a trust feedback recommendation algorithm based on directed trust graph (DTG), which is able to propagate trust relationship. In our approach, there is no need to compute similarity between users, but utilize the trust relation between them to conduct prediction calculation. Based on the analysis of human trust perception, we incorporate the process into our recommendation algorithm. Experimental evaluation on real life Epinions datasets shows that the effectiveness and practicability of our approach.

Study of Spatial Pattern of County-level E-commerce and Its Influential Factors in Zhejiang Province, China

“…Most of the studies focused on the e-commerce concept definition [8], trust issues [9][10], personalized recommendation [11], consumer behavior patterns [12], big data analysis [13], etc. E-commerce space distribution characteristics and its related influencing factor analyses belong to the category of economic geography.…”

Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

“…The concept of trust has been studied by scholars all over the world in diverse contexts and from various disciplines, including Economics, Management, Computer Science and Sociology [8], [13], [14]. In the field of economic research, trust is explained from the theory of rational choice, and defined as a rational action to make a choice of whether to give a trust after careful thought and cost calculation.…”

A preference elicitation method based on bipartite graphical correlation and implicit trust

“…It can also be found from the results that although the bipartite correlation and implicit trust both have influences on acquiring the preferences, the bipartite correlation reflect users' preference more accurately. We compare our algorithm with RNCF [16] and GBR [19] in the test data set of MovieLens and conceal the users' data randomly by ABO. The GBR uses directed trust graph to generate recommendation which is one of the latest applications of graph model on recommender systems.…”

“…Inspired by graph-based model, many researchers use the user-item bipartite graph to describe the historical preference [17,18,19], representing the standard user-item rating matrix as graph vertexes and edges to get predictions. Such as, Liu et al [17] focus on the combination of collaborative filtering and network structure, Lee et al [18] try to find novel recommendations among relevant items based on graph model, Zhong et al [19] study on the directed trust graph based recommendation methods. Their experimental results proved that graph-based algorithms are excellent in mining the complex relationship among users.…”