基于评论和物品描述的深度学习推荐算法

doi:10.11896/jsjkx.210200170

摘要/Abstract

摘要： 评论文本中蕴含着丰富的用户和物品信息,将其应用于推荐算法有助于缓解数据稀疏问题,提高推荐准确度。然而,现有的基于评论的推荐模型对评论文本的挖掘不够充分和有效,并且大多忽视了用户兴趣随时间的迁移和蕴含物品属性的物品描述文档,使得推荐结果不够准确。基于此,文中提出了一种基于深度语义挖掘的推荐模型(Deep Semantic Mining based Recommendation,DSMR),通过深度挖掘评论文本和物品描述文档的语义信息,更精确地提取用户特征和物品属性特征,从而实现更准确地推荐。首先,所提模型利用BERT预训练模型来处理评论文本和物品描述文档,深度挖掘用户特征和物品属性,有效缓解了数据稀疏和物品冷启动问题;然后,利用前向LSTM来关注用户偏好随时间产生的变化,得到了更精确的推荐;最后,在模型训练阶段,将实验数据按1～5分1∶1∶1∶1∶1等量随机抽取,保证每个分值的数据量相等,使结果更加准确,模型鲁棒性更强。在4个常用的亚马逊公开数据集上进行实验,结果表明,以均方根误差为评价指标,DSMR推荐结果的误差比2个仅基于评分数据的经典推荐模型至少平均降低了11.95%,同时优于基于评论文本的3个最新推荐模型,且比其中最优的模型平均降低了5.1%。

关键词: 冷启动, 评论文本, 深度学习, 数据稀疏性, 推荐算法, 物品描述

Abstract: Reviews contain rich user and item information,which helps to alleviate the problem of data sparsity.However,the existing recommendation model based on reviews is not sufficient and effective enough to mine the review texts,and most of them ignore the migration of user interest over time and the item description documents containing the item attribute,which makes the recommendation result not accurate enough.In this paper,a deep semantic mining based recommendation model (DSMR) is proposed.By mining the semantic information of review texts and item description documents in depth,user characteristics and item attributes can be extracted more accurately,so as to realize more accurate recommendation.Firstly,the BERT pre-training model is used to process the comment text and item description document,and the user characteristics and item attributes are excavated deeply,which effectively alleviated the problems of data sparse and item cold start.Then,the forward LSTM is used to pay attention to the change of user preferences over time,and more accurate recommendations are obtained.Finally,in the model training stage,the experimental data are randomly selected from 1 to 5 points at 1∶1∶1∶1∶1 to ensure the same amount of data for each score value,so as to make the results more accurate and the model more robust.Experiments on four commonly used Amazon open datasets show that the root mean square error (RMSE) of DSMR is at least 11.95% lower than the two classical recommendation models based only on rating data,and it is better than the three new recommendation models based only on review text,and 5.1% lower than the optimal model.

Key words: Cold start, Data sparsity, Deep learning, Item description, Recommendation algorithm, Review

中图分类号:

TP391

王美玲, 刘晓楠, 尹美娟, 乔猛, 荆丽娜. 基于评论和物品描述的深度学习推荐算法[J]. 计算机科学, 2022, 49(3): 99-104. https://doi.org/10.11896/jsjkx.210200170

WANG Mei-ling, LIU Xiao-nan, YIN Mei-juan, QIAO Meng, JING Li-na. Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions[J]. Computer Science, 2022, 49(3): 99-104. https://doi.org/10.11896/jsjkx.210200170

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