Personalized recommendation via an improved NBI algorithm and user influence model in a Microblog network

Jie Lian, Yun Liu, Zhen-jiang Zhang and Chang-ni Gui

Physica A: Statistical Mechanics and its Applications, 2013, vol. 392, issue 19, 4594-4605

Abstract: Bipartite network based recommendations have attracted extensive attentions in recent years. Differing from traditional object-oriented recommendations, the recommendation in a Microblog network has two crucial differences. One is high authority users or one’s special friends usually play a very active role in tweet-oriented recommendation. The other is that the object in a Microblog network corresponds to a set of tweets on same topic instead of an actual and single entity, e.g. goods or movies in traditional networks. Thus repeat recommendations of the tweets in one’s collected topics are indispensable. Therefore, this paper improves network based inference (NBI) algorithm by original link matrix and link weight on resource allocation processes. This paper finally proposes the Microblog recommendation model based on the factors of improved network based inference and user influence model. Adjusting the weights of these two factors could generate the best recommendation results in algorithm accuracy and recommendation personalization.

Keywords: Personalized recommendation; Microblog recommendation; Network based inference; Resource allocation; Collaborative filtering; Complex network (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:392:y:2013:i:19:p:4594-4605

DOI: 10.1016/j.physa.2013.05.025

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