Optimizing an English text reading recommendation model by integrating collaborative filtering algorithm and FastText classification method.

To comprehend the genuine reading habits and preferences of diverse user cohorts and furnish tailored reading recommendations, this study introduces an English text reading recommendation model designed specifically for long-tail users. This model integrates collaborative filtering algorithms with the FastText classification method. Initially, the integrated collaborative filtering algorithm is explicated, followed by the calculation of the user's interest distribution across various types of English texts, achieved through an enhanced Ebbinghaus forgetting curve and analysis of user reading behaviors. Subsequently, an intelligent English text reading recommendation is generated by amalgamating collaborative filtering algorithms with association rule-based recommendation algorithms. Through optimization of the recommendation generation process, the model's recommendation accuracy is enhanced, thereby augmenting the performance and user satisfaction of the recommendation system. Finally, a comparative analysis is conducted with respect to the Top-N algorithm model, matrix factorization-based algorithm model, and FastText classification model, illustrating the superior recommendation accuracy and F-Measure value of the proposed model. The study findings indicate that when the recommendation list contains 10, 30, 50, and 70 texts, the recommendation accuracy of the proposed algorithm model is 0.75, 0.79, 0.8, and 0.74, respectively, outperforming other algorithms. Furthermore, as the number of texts increases, the F-Measure of all four models gradually improves, with the final F-Measure of the proposed model reaching 0.81. Notably, the F-Measure of the English text reading recommendation model proposed in this study significantly surpasses that of the other three recommendation methods. Demonstrating commendable performance in recall rate, root mean square error, normalized cumulative gain, precision, and accuracy, the model adeptly reflects user reading interests, thereby enhancing the accuracy of text recommendations and the overall system performance. The study findings offer crucial insights and guidance for enhancing the accuracy and overall efficacy of English text recommendation systems.

Cross-domain information fusion and personalized recommendation in artificial intelligence recommendation system based on mathematical matrix decomposition.

Given the challenges of inter-domain information fusion and data sparsity in collaborative filtering algorithms, this paper proposes a cross-domain information fusion matrix decomposition algorithm to enhance the accuracy of personalized recommendations in artificial intelligence recommendation systems. The study begins by collecting Douban movie rating data and social network information. To ensure data integrity, Levenshtein distance detection is employed to remove duplicate scores, while natural language processing technology is utilized to extract keywords and topic information from social texts. Additionally, graph convolutional networks are utilized to convert user relationships into feature vectors, and a unique thermal coding method is used to convert discrete user and movie information into binary matrices. To prevent overfitting, the Ridge regularization method is introduced to gradually optimize potential feature vectors. Weighted average and feature connection techniques are then applied to integrate features from different fields. Moreover, the paper combines the item-based collaborative filtering algorithm with merged user characteristics to generate personalized recommendation lists.In the experimental stage, the paper conducts cross-domain information fusion optimization on four mainstream mathematical matrix decomposition algorithms: alternating least squares method, non-negative matrix decomposition, singular value decomposition, and latent factor model (LFM). It compares these algorithms with the non-fused approach. The results indicate a significant improvement in score accuracy, with mean absolute error and root mean squared error reduced by 12.8% and 13.2% respectively across the four algorithms. Additionally, when k = 10, the average F1 score reaches 0.97, and the ranking accuracy coverage of the LFM algorithm increases by 54.2%. Overall, the mathematical matrix decomposition algorithm combined with cross-domain information fusion demonstrates clear advantages in accuracy, prediction performance, recommendation diversity, and ranking quality, and improves the accuracy and diversity of the recommendation system. By effectively addressing collaborative filtering challenges through the integration of diverse techniques, it significantly surpasses traditional models in recommendation accuracy and variety.

Enhancing e-commerce recommendation systems through approach of buyer's self-construal: necessity, theoretical ground, synthesis of a six-step model, and research agenda.

The current recommendation system predominantly relies on evidential factors such as behavioral outcomes and purchasing history. However, limited research has been conducted to explore the use of psychological data in these algorithms, such as consumers' self-perceived identities. Based on the gap identified and the soaring significance of levering the non-purchasing data, this study presents a methodology to quantify consumers' self-identities to help examine the relationship between these psychological cues and decision-making in an e-commerce context, focusing on the projective self, which has been overlooked in previous research. This research is expected to contribute to a better understanding of the cause of inconsistency in similar studies and provide a basis for further exploration of the impact of self-concepts on consumer behavior. The coding method in grounded theory, in conjunction with the synthesis of literature analysis, was employed to generate the final approach and solution in this study as they provide a robust and rigorous basis for the findings and recommendations presented in this study.

Application of big data search based on collaborative filtering algorithm in cross-border e-commerce product recommendation.

With the deepening of cross-border e-commerce, the trend of buying and selling goods through the Internet is rising. It is necessary to establish a cross-border e-commerce platform that meets the above functions, and improve the ability to process big data in search. For example, the emergence of large amounts of data can not only help users make choices, but also increase the difficulty of users in choosing. At present, there are many problems in the big data search system in the market, such as inaccurate user personality analysis and low importance of product recommendation. E-commerce is developing rapidly in the new era, and new users are increasing every day. Many researchers invest in finding excellent cross-border e-commerce recommendation system as a business platform. The number of information in cross-border e-commerce shows a rapid growth pattern, and the rapid growth of data and information has seriously affected people's judgment. The big data search system based on collaborative filtering algorithm can meet the product recommendation system of cross-border e-commerce. The user matrix label is an attribute of construction. For the label quantification, the new user preference is the model of building the label, and the concept of weight is added to the label. The collaborative filtering algorithm works based on the created weight label.

Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.

BACKGROUND: Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. METHODS: In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. RESULTS: The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. CONCLUSION: With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.