Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks

Abstract

With the rapid growth of the Internet, a wealth of movie resources are readily available on the major search engines. Still, it is unlikely that users will be able to find precisely the movies they are more interested in any time soon. Traditional recommendation algorithms, such as collaborative filtering recommendation algorithms only use the user’s rating information of the movie, without using the attribute information of the user and the movie, which has the problem of inaccurate recommendations. In order to achieve personalized accurate movie recommendations, a movie recommendation algorithm based on a multi-feature attention mechanism with deep neural networks and convolutional neural networks is proposed. In order to make the predicted movie ratings more accurate, user attribute information and movie attribute information are added, user network and movie network are presented to learn user features and movie features, respectively, and a feature attention mechanism is proposed so that different parts contribute differently to movie ratings. Text features are also extracted using convolutional neural networks, in which an attention mechanism is added to make the extracted text features more accurate, and finally, personalized movie accurate recommendations are achieved. The experimental results verify the effectiveness of the algorithm. The user attribute features and movie attribute features have a good effect on the rating, the feature attention mechanism makes the features distinguish the degree of importance to the rating, and the convolutional neural network adding the attention mechanism makes the extracted text features more effective and achieves high accuracy in MSE, MAE, MAPE, R2, and RMSE indexes.

Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans

“…The transformer architecture, originally used for natural language processing, has led to a significant leap forward in creating adaptive systems that can capture long-range dependencies in sequences of user-item interactions and improving recommendation performance and interpretability. Kang, Wang-Cheng, and Julian McAuley [39] introduced self-attentive sequential recommendations (SASRec), while Yu, Saisai et al [40] proposed personalized movie recommendation algorithms that fuse visual and textual features using multi-head attention with neural networks that can address data sparsity and cold start problems. Chen, Qiwei et al [41] presented a Behavior Sequence Transformer for E-commerce Recommendation in Alibaba, a model designed to enhance recommendation systems within the e-commerce domain.…”

Movie recommender system app based on natural language processing

“…If we apply as in the previous case a simple sorting, we will have results in which the common elements can be identified [Table 5]: 1), (1,3), (2,1), (3,3), (4, 1), (5,1), (6,3), (7,1), (8,1), (9,1), (10,1), (11,1), (12, 1), (13, 1), ..]…”

“…Incorporating a feature attention mechanism contributes significantly to discerning the relative significance of these attributes. Moreover, the attention mechanism integrated into the convolutional neural network enhances the precision of text feature extraction, yielding a commendable level of accuracy across various evaluation metrics, including but not limited to Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R-squared (R2), and Root Mean Squared Error (RMSE) [9].…”