计算机科学 ›› 2018, Vol. 45 ›› Issue (7): 172-177.doi: 10.11896/j.issn.1002-137X.2018.07.030

• 人工智能 • 上一篇    下一篇

任务推荐中考虑任务关联度与时间因素的改进OCCF方法

王刚,王含茹,胡可,贺曦冉   

  1. 合肥工业大学管理学院 合肥230009
  • 收稿日期:2017-01-19 出版日期:2018-07-30 发布日期:2018-07-30
  • 作者简介:王 刚(1980-),男,副研究员,主要研究方向为商务智能与商务分析,E-mail:wgedison@gmail.com(通信作者);王含茹(1994-),女,硕士生,主要研究方向为社会化推荐;胡 可(1996-),男,主要研究方向为任务推荐;贺曦冉(1994-),女,硕士生,主要研究方向为社会化推荐。
  • 基金资助:
    本文受国家自然科学基金(71471054,91646111),安徽省自然科学基金(1608085MG150)资助。

Improved OCCF Method Considering Task Relevance and Time for Task Recommendation

WANG Gang, WANG Han-ru, HU Ke ,HE Xi-ran   

  1. School of Management,Hefei University of Technology,Hefei 230009,China
  • Received:2017-01-19 Online:2018-07-30 Published:2018-07-30

摘要: 随着众包系统的兴起,人们对众包系统的关注逐渐增多。基于众包系统中的任务推荐,研究者大多将用户对任务的行为数据转化为评分,但没有考虑任务关联关系以及用户兴趣变化对推荐结果的影响。为此,提出一种考虑任务关联度与时间因素的改进OCCF方法,以对任务进行推荐。一方面,在负例抽取阶段引入兴趣遗忘函数,并根据用户活跃度抽取一定数量的负例;另一方面,在概率矩阵分解阶段融合任务相似度信息以进行分解。将所提出的方法应用于众包系统的任务推荐中,利用威客任务中国的数据集进行了实验。实验结果表明,与主流方法相比,所提方法取得了更好的结果,能有效地提高推荐质量。

关键词: OCCF, 任务推荐, 时间因素, 推荐系统, 用户兴趣变化

Abstract: With the development of crowdsourcing system,researchers pay more attention to the crowdsourcing system.Based on the task recommendation of crowdsourcing,most of research scholars convert the behavior data into rate data,without considering the relationship between tasksor the influence caused by the change of user interest on the recommendation results.Therefore,this paper proposed an improved OCCF method considering the task relevance and the time factor to recommend task.On the one hand,this paper introduced a forgetting function when extracting the negative cases,and extracted a certain number of negative cases according to users’ activity.On the other hand,it merged the similarity information of tasks in the probability matrix factorization phase.The proposed method was further applied to recommend tasks in the crowdsourcing system.This paper used the data set of Taskcn to conduct experiments.The experimental results show that the proposed method achieves better results,and effectively improves the quality of recommendation compared with the mainstream methods.

Key words: Changes of user interest, OCCF, Recommendation system, Task recommendation, Time factor

中图分类号: 

  • TP391.3
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