基于Seq2seq模型的推荐应用研究

摘要/Abstract

摘要： 日常生活的信息纷繁复杂,因此需要推荐系统来帮助人们进行信息筛选。传统的推荐系统将推荐过程看成是静态的,缺少对序列数据短期或长期的依赖关系的研究。循环神经网络由于在处理序列化数据时有突出的表现,因此可应用到具有序列特征的推荐数据中。文中采用循环神经网络的seq2seq模型来构造这种推荐系统,将推荐过程看作一个序列的翻译过程或答案生成的过程,利用大量用户以往的交互数据,找出其中的频繁模式,将其应用到其他用户对物品的行为预测中。实验在两个常用数据集上进行,使用BLEU衡量推荐结果,实验结果表明:该方法可以做出序列化的推荐。该方法只需要用户和物品的互动数据,摆脱了评分矩阵,避免了数据稀疏性的问题。

关键词: Seq2seq模型, 推荐系统, 循环神经网络

Abstract: There is enormous information around us in daily basis which lead to the recommander systems to filter out the pure gold.The traditional recommander systems have been regarded as static,and lack of the research about the long or short term dependency of data.Considering the outstanding perform of recurrent neural network in tackling the sequence data,recommander system based on seq2seq model was built.The process of recommandation can be viewed as a process of sequence translation or a process of answer generation,and the model make uses of the used interactive sequence data to learn the inherent frequent patterns,then makes the prediction of other users’ actions with items.Two datasets usually used for recommender system test are involved in the experiments,which measured by the BLEU.The results show that the method can make the sequence recommendation.The model only needs the interactive data between users and items,and gets rid of the rating matrix,thus avoids the sparsity problem.

Key words: Recommender system, Recurrent neural networkm, Seq2seq model

中图分类号:

TP391.4

陈俊航, 徐小平, 杨恒泓. 基于Seq2seq模型的推荐应用研究[J]. 计算机科学, 2019, 46(6A): 493-496. https://doi.org/

CHEN Jun-hang, XU Xiao-ping, YANG Heng-hong. Research on Recommendation Application Based on Seq2seq Model[J]. Computer Science, 2019, 46(6A): 493-496. https://doi.org/

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