A graph model for E‐commerce recommender systems

Abstract

Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.

Attribute Aware Anonymous Recommender Systems

“…There are in general four recommendation approaches: collaborative filtering, content-based, hybrid and knowledge-based [7].…”

Research on Algorithm Recommended by Online Education for Big Data

“…In order to provide effective and accurate recommendation set to users, while guaranteeing the performance of the recommender system and other non-functional requirements, the researchers and enterprises have put forward many personalized recommendation algorithms, such as Item-based collaborative filtering recommendation algorithm [3,4] , User-based collaborative filtering recommendation algorithm [5] , Content-based recommendation algorithm [6,7,8] , Cluster-based collaborative filtering recommendation algorithm [9] , SVD-based collaborative filtering recommendation algorithm [10] and image-based collaborative filtering recommendation algorithm [11][12] , etc.. These algorithms uses data mining techniques to conduct in-depth analysis of user data and project data to obtain the interest characteristics and the specific patterns of behavior for users, and thus to provide personalized recommendation for users.…”

“…As for the difference from the classic content-based recommendation methods, the algorithm is mainly to conduct analysis and mining the user groups with high similarity to the target users, or item set similar to target item, and then use the user group and item set to provide personalized recommendation for users. According to the difference of used business association, the collaborative filtering recommendation algorithm can be divided into User-based collaborative filtering algorithm [5] , Item-based collaborative filtering algorithm [8,4] and Model-based collaborative filtering algorithm [11][12] , etc.. Each algorism in personalized recommendation system has its advantages and disadvantages, and also a certain degree of complementarity in preferences. So In the current Web recommendations will not adopt one single recommendation mechanism and strategy, but to integrate multiple methods, namely Hybrid Recommendation, thus to achieve a better effect of recommendation [13,14,15] .…”

An Online Personalized Recommendation Model Based on Bayesian Networks

“…In this model, the links is associated with customers and merchandises. Zan Huang [8] gave a two-layer graph model. It presented that the nodes in different layers denoted the merchandises and users respectively.…”