Abstract: 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-RT²NI 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-RT²NI exhibits superior rating prediction accuracy,yielding an average RMSE reduction of 3.0657%.

Key words: Recommendation model, Rating matrix, Review text, Review timeliness, Trusted neighbor influence, Multi-feature fusion