Analysis of Brand Visual Design Based on Collaborative Filtering Algorithm

With the continuous development of China’s social economy, the competitiveness of brand market is gradually increasing. In order to improve their own level in brand building, major enterprises gradually explore and study visual communication design. Brand visual design has also received more and more attention. Building a complete and rich visual design system can improve the brand level and attract users to consume. Based on the abovementioned situation, this paper proposes to use collaborative filtering algorithm to analyze and study brand visual design. Firstly, a solution is proposed to solve the problem of low accuracy of general recommendation algorithm in brand goods. Collaborative filtering algorithm is used to analyze the visual communication design process of enterprise brand. Research on personalized image design according to consumers’ trust and recognition of brand design is conducted. In traditional craft brand visual design, we mainly study the impact of image design on consumer behavior. The brand loyalty model is used to predict and analyze the visual design effect. Also, the user’s evaluation coefficient is taken as the expression of brand visual design recognition. Finally, the collaborative filtering algorithm is optimized to improve the consumer similarity based on the original algorithm. The results show that the brand visual design using collaborative filtering algorithm can help enterprises obtain greater benefits in their own brand construction. It provides effective data help in the development of traditional craft brands.

Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

Design and Application of Handicraft Recommendation System Based on Improved Hybrid Algorithm

With the rapid development of online e-commerce, traditional collaborative filtering algorithms have the disadvantages of data set reduction and sparse matrix filling cannot meet the requirements of users. This paper takes handicrafts as an example to propose the design and application of handicraft recommendation system based on an improved hybrid algorithm. Based on the theory of e-commerce system, through the traditional collaborative filtering algorithm of users, the personalized e-commerce system of hybrid algorithm is designed and analyzed. The personalized e-commerce system based on hybrid algorithm is further proposed. The component model of the business recommendation system and the specific steps of the improved hybrid algorithm based on user information are given. Finally, an experimental analysis of the improved hybrid algorithm is carried out. The results show that the algorithm can effectively improve the effectiveness and exemption of recommending handicrafts. What’s more, it can reduce the user item ratings of candidate set and improve accuracy of the forecast recommendation.

Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering

At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms.

Interior Design Scheme Recommendation Method Based on Improved Collaborative Filtering Algorithm

The recommendation engine is similar to the function of the product recommender in our real life, which provides great convenience for people to choose the appropriate decoration scheme in the process of interior design and decoration. A home improvement website or company can design a suitable recommendation algorithm to provide home improvement program recommendation services for users with decoration needs. After understanding the user behavior of the home decoration website, this paper proposes an interior design scheme recommendation method based on an improved collaborative filtering algorithm. The method designs a collaborative filtering algorithm that combines multilayer hybrid similarity and trust mechanisms. Fuzzy set membership function is introduced to correct users’ rating similarity, and users’ interest vector is extracted to calculate users’ preference for different types of items. The algorithm dynamically fuses those two aspects to obtain the mixed similarity of users; meanwhile, the user’s hybrid similarity and trust are fused in an adaptive model. Then, the user neighbor data set generated based on the overall similarity of users is used as a training set, taking the item scores and features into consideration. On the one hand, the users and the projects are taken into account as well. The final prediction score is more accurate, and the recommendation effect is better. The experimental results show that this method can recommend interior design schemes with high performance, and its performance is better than other methods.

Game Teaching Method in Preschool Education Based on Big Data Technology

The traditional preaching way of imparting knowledge can only stifle children’s imagination, creativity, and learning initiative a little bit, which is harmful to children’s healthy and happy growth. This paper combines big data technology to evaluate the effect of game teaching method in preschool education, analyzes the teaching effect of game teaching method in preschool education, and combines big data technology to find problematic teaching points. Based on the collaborative filtering algorithm of preschool children, this paper estimates the current preschool children’s score for the game by referring to the scores of neighbor preschool children on the predicted game and constructs an intelligent model. Finally, this paper combines experimental research to verify the model proposed in this paper. From the experimental research, it can be seen that the method proposed in this paper has a certain effect.

Ideological and Political Education Recommendation System Based on AHP and Improved Collaborative Filtering Algorithm

Aiming to solve the problem that ideological and political education courses in universities are not targeted enough and cannot form personalized recommendations, this paper proposes an ideological and political education recommendation system based on analytic hierarchy process (AHP) and improved collaborative filtering algorithm. Firstly, considering the time effect of student scoring, the recommendation model is transformed into Markov decision process. Then, by combining the collaborative filtering algorithm with reinforcing learning rewards and punishments, an optimization model of student scoring based on timestamp information is constructed. To quantify the degree of students' preference for courses, the analytic hierarchy process is used to convert the students' behavior data into the preference value of courses. To solve the problem of data scarcity, the missing values are predicted by the prediction score rounding filling and the optimization boundary completion method. Experimental results show that the feasibility of the proposed system is verified, and the system has vital accuracy and convergence performance. The ideological and political education recommendation system proposed in this paper has important reference significance for promoting ideological and political education in the era of big data.

A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

The Neighbourhood-based collaborative filtering (CF) algorithm has been widely used in recommender systems. To enhance the adaptability to the sparse data, a CF with new similarity measure and prediction method is proposed. The new similarity measure is designed based on the Hellinger distance of item labels, which overcomes the problem of depending on common-rated items (co-rated items). In the proposed prediction method, we present a new strategy to solve the problem that the neighbour users do not rate the target item, that is, the most similar item rated by the neighbour user is used to replace the target item. The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction. The experimental results on two benchmark datasets both confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.