Fast Transformation Method of Service Centre Data Model in Power Grid Resource Service

Traditional collaborative filtering recommendation algorithm is one of the methods to solve the information overloading problem in E-Commerce. However, there are four urgent problems in this algorithm namely data sparse, cold start, attack-resistant and scalability. This paper makes a trust propagation model called TPM; proposes a hybrid index called TS index and a novel collaborative filtering recommendation algorithm called TPCF using TPM and TS index. The results of experiments using the dataset of Epinions.com, a popular ecommerce review website, show that TPCF is more attack-resistant and improves the precision and coverage rate compared with the traditional collaborative filtering recommendation algorithm using Pearson's correlation coefficient. TPCF has a better performance against the traditional collaborative filtering recommendation algorithm on the problems of data sparse, cold start and attack-resistant.

In order to alleviate data sparsity and cold-start problems of traditional collaborative filtering recommendation algorithm, a meta-based fusion heterogeneous information network recommendation algorithm is adopted in this paper. The algorithm integrates the characteristics of multi-relationship social network and user’s preference degree and adopts a universal representation for different types of data. A meta-graph-based similarity measurement method makes it possible to better capture the semantic relationships between different types of data and a score matrix decomposition method based on multiple meta-graphs is used. Each project and user generates a variety of potential feature matrices based on different meta-graphs. Effectively integrates multiple feature matrices into a unified, final implicit feature matrix. We use each factor of each line of the implicit feature matrix as a neural network. The input node predicts user ratings by optimizing the scoring neural network. Finally, we used the data set provided by the Yelp website to do user rating prediction experiments, which proved the accuracy of this algorithm is 5% higher than the traditional collaborative filtering recommendation algorithm.

To solve the problems of cold start and data sparseness existing in traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on user attributes and item scoring is proposed. Firstly, we improve the credibility of user similarity and explore the potential interests of users, a new user rating similarity calculation method is constructed by introducing confidence, item popularity, and Pearson weighting. Secondly, we construct a user attribute similarity measurement method by introducing cultural distance, age attribute similarity, and user label similarity. Finally, user rating similarity and user attribute similarity are weighted to form a new similarity measurement model. Through simulation comparison between the collaborative filtering recommendation algorithm and the traditional recommendation algorithm, our results show that the collaborative filtering recommendation algorithm can effectively improve the accuracy of recommendations and the diversity of results and effectively alleviate the problem of data sparseness.

In order to solve the data sparsity problem existing in the traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm for scenic spots based on multi-dimensional feature clustering was proposed. Firstly, the users are clustered and classified according to the feature vector. Then we determine the category of the target user. Building user-scenic spot score matrix, on this basis, the user-scenic spot attention matrix is added. In order to optimize the traditional similarity recommendation algorithm, the attention matrix and the score matrix are linearly combined with the balance factor to calculate the similarity between users. In addition, the similarity threshold is introduced to determine the similar neighbor set. And recommend scenic spots to the target user according to the users in similar neighbor set. Finally, the MAE of the algorithm and the traditional recommendation algorithm are compared by using the tourism related data of Qingdao City crawled on the Mafengwo tourism website. The experimental results show that the algorithm proposed in the paper not only reduces the sparsity of data, but also improves the recommendation accuracy and has better stability.

With the rapid development of the Internet, e-commerce plays an important role in people’s lives, and the recommendation system is one of the most critical technologies. However, as the number of users and the scale of goods increase sharply, the traditional collaborative filtering recommendation algorithm has a large computational complexity in the part of calculating the user similarity, which leads to a low recommendation efficiency. In response to the above problems, this paper introduces the concept of quantum computing theory. The user score vector is first prepared into a quantum state, the similarity score is calculated in parallel, then the similarity information is saved into the quantum bit, and finally the similar user is searched by the Grover search algorithm. Compared with the traditional collaborative filtering recommendation algorithm, the time complexity of the collaborative filtering recommendation algorithm based on Grover algorithm can be effectively reduced under certain conditions.

The weight of all users' score is the same in traditional collaborative filtering recommendation algorithm, and it doesn't consider the shift of users' preferences with time, so recommendation quality is poor. In order to avoid the problems above, a novel collaborative filtering algorithm based on shift of users' preferences is presented: The method adjusts the weight of users' score according to time, improves users' similarity with a gradual forgetting function, and considers the impacts on similarity between users brought by the shift of users' preferences, then clusters users in terms of their features, reduces chosen space of the nearest neighbors. The experiment result shows that this method has better recall and precision than traditional collaborative filtering recommendation algorithm, and it can effectively improve recommendation quality.

In the collaborative filtering recommendation algorithm, the similarity calculation plays an important role in the recom-mendation quality. For the traditional collaborative filtering recommendation algorithm, the similarity calculation is performed by a single user score, and the user's demand for the item cannot be accurately reflected. In order to solve this problem, the research proposes a distance-based scenic recommendation algorithm. The algorithm introduces the distance between the user and the item when performing the similarity calculation, then calculating the user's score on target scenic spots for recommendation. The experimental results show that, compared with the traditional collaborative filtering recommendation algorithm based on user score, the result of the distance-based scenic spot recommendation algorithm have some improvement in root-mean-square error, mean-absolute error, coverage, precision and f-measure.

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

With the increase of volume, velocity, and variety of big data, the traditional collaborative filtering recommendation algorithm, which recommends the items based on the ratings from those like-minded users, becomes more and more inefficient. In this paper, two varieties of algorithms for collaborative filtering recommendation system are proposed. The first one uses the improved k-means clustering technique while the second one uses the improved k-means clustering technique coupled with Principal Component Analysis as a dimensionality reduction method to enhance the recommendation accuracy for big data. The experimental results show that the proposed algorithms have better recommendation performance than the traditional collaborative filtering recommendation algorithm.

Personalised recommendation systems are a common feature of e-commerce, streaming services, and other fields. However, they are not common in the field of interior environment design. Existing international research tends to prioritise deep learning–based recommendation algorithms. These methods rely heavily on large-scale data and complex models to improve the accuracy of recommendations. However, they often face significant limitations. Therefore, this paper proposes a personalised recommendation model for interior environment design based on clustering collaborative filtering algorithm and user portrait. The model improves the traditional collaborative filtering recommendation algorithm. Based on the established user profile and improved collaborative filtering recommendation algorithm, the constructed model is verified through performance analysis and practical application test. The experimental results show that when the number of neighbours is 20, the MAE of the model is 0.7035, which has better personalised recommendation performance. In addition, in the personalised recommendation application of interior environment design, the click-through rate of the model is increased by 0.115, and the purchase conversion rate of users is increased by about 0.270, which further proves the effectiveness of the model. The results show that this model can provide more accurate, diversified, and personalised indoor environment design solutions to meet the individual needs of users.

In order to solve the contradiction between the free course selection mode and blind course selection, this paper combines the knowledge-based recommendation algorithm model and the memory based collaborative filtering recommendation model, and proposes an improved collaborative filtering recommendation algorithm to mine the implicit learning order and association relationship between courses and provide recommendations for freshmen, This algorithm solves the cold start problem caused by the data sparsity problem of the traditional collaborative filtering recommendation algorithm, so that the algorithm can still give high-quality recommendation results when the initial data is extremely sparse. The experimental results show that compared with the traditional recommendation algorithm, the accuracy and recall of this method have been improved, good recommendation results are obtained on real data sets.

In order to solve the problem of information overload, a large number of personalized recommendation algorithms merged. Data sparsity is one of the difficult problems in these algorithms. Aiming at this, a novel collaborative filtering algorithm which introduces the trust into the traditional collaborative filtering recommendation algorithm is proposed in this paper. The proposed algorithm first measures the comprehensive trust by weighting the direct trust and the indirect trust, then obtains the similarity using Pearson model, and calculates the trusted similarity for prediction at last. To verify the performance of the proposed algorithm, the Mean Absolute Error (MAE) between the proposed algorithm with adaptive coordination factor and the traditional collaborative filtering recommendation algorithm with empirical factor is compared. The result shows that the proposed algorithm has better prediction accuracy.

With information technology and the Internet developing fast, people gradually walk out of the time of information deficient and enter the era of information overload. Whether information consumers or information producers are faced with big challenge: how to obtain or sell the information. Recommendation system is a key to this problem. Traditional recommendation system focuses on connecting user interest and items, and recommends items which match user interest. However, all these algorithms ignore the context which users are in. In terms of this problem, this paper presents a novel collaborative filtering recommendation algorithm based on user location context. Firstly, this algorithm defines user location attenuation function to calculate the relations between user locations, then combines this function with traditional Pearson similarity method to get similarity between users, finally, uses the traditional collaborative filtering recommendation algorithm to realize preference prediction and recommendation. Experiments show that this algorithm which has location information taken into account can improve recommendation quality for traditional collaborative filtering recommendation algorithms.

With the dramatic increase of the amount of network information, the phenomenon of merchandise information overload has become more and more serious. Thus, the e-commerce sites are required to provide the most needed information for customers to attract their attention. The traditional recommendation algorithm ignores the connections among user's own attributes and the changes in project scoring with each passing day. With the research of the traditional collaborative filtering recommendation algorithm, the paper put an emphasis on the combination of user model and user project matrix, and introduced the data of user attributes and the variation values of project scoring over time into the traditional recommendation algorithm. By experiment, the validity of the algorithm was confirmed.

Since cross-border e-commerce involves the export and import of commodities, it is affected by many policies and regulations, resulting in some special requirements for the recommendation system, which makes the traditional collaborative filtering recommendation algorithm less effective for the cross-border e-commerce recommendation system. To address this issue, a simple yet effective cross-border e-commerce personalized recommendation is proposed in this paper, which integrates fuzzy association rule and complex preference into a recommendation model. Under the constraint of fuzzy association rules, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference. Compared with the traditional recommendation algorithm, the improved algorithm reduces the impact of data sparsity. The experiment also verifies that the improved fuzzy association rule algorithm has a better recommendation effect than the existing state-of-the-art recommendation models. The recommendation system proposed in this paper has better generalization and has the performance to be applied to real-life scenarios.