Research on Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interaction Features

Research on Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interaction Features

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To investigate the impact of cross-grained sentiments on user feature representation and address the issue of data sparsity, this paper proposes a Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interaction Features (ICSR).The algorithm begins by employing a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model and a Bi-GRU (Bidirectional Gated Recurrent Units) network to derive feature vectors from user and item reviews.Sentiment dictionaries and attention mechanisms are apple watch model m07h3ll/a then applied to assign appropriate weights to the review features of users and items, respectively.To capture a richer set of sentiment features, a cross-grained sentiment feature fusion module is introduced.

This module leverages an LDA (Latent Dirichlet Allocation) model and dependency syntactic analysis techniques to extract fine-grained sentiment features, while a word2vec pre-trained model and sentiment dictionaries are used to capture coarse-grained sentiment features.These features are then fused to form comprehensive cross-grained sentiment representations.Finally, rating interaction features are extracted using matrix factorization techniques, and all features are graham c+ cream integrated and fed into a DeepFM model for rating prediction.Experimental results on Amazon datasets demonstrate that the proposed ICSR algorithm significantly outperforms baseline algorithms in terms of recommendation performance.