A Utility-Based Recommendation Approach for Academic Literatures
Shenshen Liang, Ying Liu, Liheng Jian, Yang Gao, Lin Zhu · 2011
With the rapid growth of information on the World Wide Web, recommender system has been receiving increasing attention. In academic literature recommendation applications, existing methods recommend papers merely based on their contents or cited frequencies, and none of them consider user's personalized requirements, such as authority, popularity, time, etc. To this end, in this paper, we propose a utility-based recommendation method. Experiments on a real-world data set show that our approach can obtain personalized recommendations without losing much quality.