RM-RT^(2)NI:融合评论时效与可信近邻影响力的推荐模型

RM-RT^(2)NI:A Recommendation Model with Review Timeliness and Trusted Neighbor Influence

基于矩阵分解的推荐模型虽然能够处理高维评分数据,但容易遭受评分数据稀疏性的困扰。基于评分和评论的推荐模型通过外加隐藏在评论中的用户偏好与物品属性信息,缓解了评分数据的稀疏性,但在特征提取时大多没有关注评论时效性和可信近邻影响力,无法获得更丰富的用户和物品特征。为进一步提高推荐精度,提出了融合评论时效与可信近邻影响力的推荐模型RM-RT^(2)NI。基于评分矩阵,该模型使用矩阵分解提取了用户偏好和物品属性的浅层特征,利用云模型和修正的用户相似度评估模型和新构建的信度评估模型提取出可信近邻影响力;基于评论文本,该模型利用BERT模型获得每条评论的隐表达,利用双向GRU提取评论间的联系,利用新构建的融合时间因子的注意力机制识别各评论的时效贡献度,以获取用户和物品的深层特征。在此基础上,将用户浅层特征、深层特征以及可信近邻影响力特征融合成用户特征,将物品浅层特征和深层特征融合成物品特征,并将它们输入全连接神经网络以预测用户-物品评分。在5组公开数据集上对RM-RM-RT^(2)NI的推荐性能进行了实验评估,结果显示,与7个基线模型相比,RM-RT^(2)NI具有更高的评分预测精度,且RMSE平均降低了3.0657%。

While recommendation models based on matrix factorization can handle high-dimensional rating data,they are prone to challenges posed by data sparsity in ratings.Recommendation models that incorporate both ratings and reviews alleviate the sparsity issue by incorporating latent user preferences and item attribute information embedded in reviews.However,these models often neglect the review timeliness and the trusted neighbor influence during feature extraction,resulting in limited acquisition of comprehensive user and item characteristics.In order to enhance accuracy further,a novel recommendation model named RM-RT^(2)NI is proposed,which integrates the review timeliness and the trusted neighbor influence.Built upon the rating matrix,this model employs matrix factorization to extract shallow features representing user preferences and item attributes.It employs cloud mo-deling,a refined user similarity assessment model,and a newly constructed credibility assessment model to capture the trusted neighbor influence.Leveraging the textual content of reviews,BERT is utilized to obtain latent representations of individual reviews.Bi-directional GRU is employed to capture inter-review relationships,while an attention mechanism incorporating timeliness is introduced to evaluate the timeliness contribution of each review,thus deriving deep features for users and items.Subsequently,the shallow and deep user features,along with the credibility-enhanced neighboring influence features,are fused to form comprehensive user representations.Similarly,shallow and deep item features are merged with this fused representation to gene-rate comprehensive item representations.These representations are then fed into a fully connected neural network to predict user-item ratings.Experimental evaluation is conducted on five publicly available datasets.The results demonstrate that,in comparison to seven baseline models,RM-RT^(2)NI exhibits superior rating prediction accuracy,yielding an average RMSE reduction of 3.0657%.