Ranking Learning Method Based on Random Walk Model
He Wanying, Yang Jianlin()
School of Information Management, Nanjing University, Nanjing 210023, China Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
[Objective] This paper tries to obtain the tagging data of training corpus for supervised ranking learning tasks. [Methods] First, we proposed a ranking learning method based on the random walk model. Then, we used this method to automatically tag the training data, which also reduced the dependency of ranking on the tags. Finally, we examined our method with the OHSUMED data set. [Results] We finished the ranking learning tasks with only half of samples tagged. Compared with algorithms based on all tagged samples, performance of the proposed method was better than the RankNet algorithm but not as good as the ListNet one. [Limitations] Our method requires a random walk for each query, which is time consuming in practice. [Conclusions] The proposed method can effectively rank the learning results of training data.
贺婉莹, 杨建林. 基于随机游走模型的排序学习方法*[J]. 数据分析与知识发现, 2017, 1(12): 41-48.
He Wanying,Yang Jianlin. Ranking Learning Method Based on Random Walk Model. Data Analysis and Knowledge Discovery, 2017, 1(12): 41-48.
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