Collaborative Filtering Technology Research Articles

Retracted: A Method for Recommending Sports Network Course Resources Based on Collaborative Filtering Technology

Retracted: A Method for Recommending Sports Network Course Resources Based on Collaborative Filtering Technology

Retracted: IoT Applications for Recommended Methods of Physical Education Online Course Resources Based on Collaborative Filtering Technology

Retracted: IoT Applications for Recommended Methods of Physical Education Online Course Resources Based on Collaborative Filtering Technology

The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method

In recent years, the clinical decision support system (CDSS) has been gradually improved, which effectively reduces the probability of doctors’ misdiagnosis or missed diagnosis. Therefore, the clinical decision support system has always been a research hotspot, deep learning and collaborative filtering technologies are developing rapidly, and more and more are applied to different fields. Based on the deep learning technology, this paper conducts in-depth research on the methods of assisted diagnosis of clinical diseases and prediction of clinical high-risk diseases in the field of CDSS. Aiming at the problem of clinical decision support system, this article analyzes the deep learning identification method in depth and is committed to applying machine deep learning to clinical decision-making, changing the lack of information and its challenges to clinical decision-making. Based on previous studies, two unsupervised learning methods based on machine learning are proposed, namely user collaborative filtering and RBM, to improve CDSS. The experimental results show that the overall performance of the RBM-based method is the best. When the missing degree of the two data sets is 30.6%, the classification accuracy rate is still more than 92.8%.

IoT Applications for Recommended Methods of Physical Education Online Course Resources Based on Collaborative Filtering Technology

Educational resources are available in different repositories and on the web. For example, these resources are in the form of courses, tutorials, simulations, tests, etc., and are available on the web. And these resources are constantly increasing. In this electronic age, it is necessary to develop systems to help people to find what they want and particularly what is more suitable to their personal subject interest. Recommender systems can help professors, researchers, and students to find the best educational resources suitable for his/her profile. The “e”-course characteristics and user profiles have to be considered, while providing the recommendations. A system can be developed where users can express a subject query to satisfy their information needs. During this stage, user characters and preferences are not considered and all users get the same results for that query. When the characteristics like language and preferences like practical and demonstrations are considered, then the information retrieval process should be improved by personalization. The set of characteristics and preferences of each user has to be stored and to be matched with e-course characteristics. There might be some characteristics that may be expressed using fuzzy values. It would be discussed how to find electronic educational resources that are suitable to the needs and characteristics of a user based on the user’s preferences and educational resources available. So, the present article recommended a method of physical education online course resources based on collaborative filtering technology.

Optimization Mold and Algorithm of Risk Control for Power Grid Corporations Based on Collaborative Filtering Technology.

With the ever-changing internal and external environmental factors of enterprises, various uncertainties and risks faced by enterprises are increasing, and the feasibility of financial meltdown is increasing. Research on financial meltdown early warning can help enterprises to prevent the occurrence of peril in advance and take resultful measures to ensure the healthy development of enterprises. If a serious financial meltdown leads to the bankruptcy of enterprises, the financial meltdown is not sudden, but a gradual process. The occurrence of financial meltdown is not only a harbinger, but also predictable. Therefore, it is an urgent question to be solved for listed corporations in China that how to mine the message with early warning function from a large amount of financial data generated in the business process of enterprises. The continuous maturity of data mining technique just solves this question. Based on collaborative filtering technique, this paper analyzes the risk control optimization mold and algorithm of power grid corporations, which is of great signification. After research, this algorithm is 30% better than the traditional algorithm, and it is suitable to be proverbially used.

A Method for Recommending Sports Network Course Resources Based on Collaborative Filtering Technology

In the process of online course resource recommendation, the output of recommendation results is often unstable. Therefore, a physical education online course resource recommendation method based on collaborative filtering technology is proposed. Firstly, the learning preference of e-learners is calculated, the frequency index of the word frequency-inverse document is defined, the correlation between courses is reflected, and the specific needs of students for PE online course resource recommendation are understood. Then, the collaborative filtering recommendation algorithm is used to generate the similarity matrix and correlation matrix, update the edge characteristics of sports online curriculum resources, collect and refine the hidden index of sports online curriculum resources, optimize the prediction rules of the neighborhood of the most similar teaching unit, and complete the recommendation of sports online curriculum resources. Experimental results show that, for 1000 keywords, the method has the highest single average matching degree, the recommendation process is stable, and the F1 value is more than 0.9, and the practical application is ensured.

Education Data-Driven Online Course Optimization Mechanism for College Student

During the recent epidemic period of COVID-19, online courses have become an important learning form for college students. However, online learning cannot communicate face to face in class and position students’ abilities accurately, and there are many problems and limitations such as one-way evaluation, for example, neglecting of process evaluation and simple evaluation form. Therefore, how to construct the evaluation system of online course teaching and make effective use of the evaluation mechanism becomes an urgent problem. Based on the big data mining of online course evaluation data, the online course evaluation optimization architecture based on process evaluation is proposed. The optimization of online course evaluation is analyzed from online course evaluation data and student comments using deep learning and collaborative filtering technology. This includes improving teacher teaching and improving student learning efficiency. Data experiment proves that the proposed algorithm can provide an optimal evaluation strategy, guarantee the students’ learning quality, and improve the efficiency of online course.

A Service Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering

With the rapid development of the Internet, the number of Web APIs is increasing. How to recommend accurate and appropriate Web APIs to mashups has become a focus and difficulty in the field of service computing. The existing methods are mainly based on collaborative filtering technology, but these methods have problems such as the data sparsity and cold start, which leads to poor recommendation effects. This paper proposes a service recommendation model based on knowledge graph and collaborative filtering. In this model, the knowledge graph connects the APIs and mashups related information to mine the potential relations between mashups and APIs, hence reducing the impact of data sparsity. All the API entities in the service knowledge graph are embedded into the low-dimensional space through the representation learning algorithm, then the distances between the API vectors are calculated to recommend the related APIs. In addition, in order to solve the cold-start problem of recommending APIs to target mashups that have no APIs usage, the similarities of functional sets extracted from mashups are calculated to recommend APIs for target mashups. At the same time, the model obtains the Mashup-API usage record, using the technology of collaborative filtering to recommend appropriate APIs to target mashups. Finally, the similarities of the above recommended APIs are normalized and sorted to form the final recommendation result. The experimental results show that our proposed model significantly improves the accuracy of service recommendation.

Integrating Stacked Sparse Auto-Encoder Into Matrix Factorization for Rating Prediction

Currently, collaborative filtering technology has been widely used in personalized recommender systems. The problem of data sparsity is a severe challenge faced by traditional collaborative filtering methods based on matrix factorization techniques. A lot of improved collaborative filtering methods have been proposed to alleviate the data sparsity problem; However, due to the sparsity of the user rating matrix, the latent factor learned by these improved methods may be not efficient. In this paper, we propose a novel recommendation algorithm named SSAERec by integrating stacked sparse auto-encoder into matrix factorization for rating prediction, which can learn effective representation from user-item rating matrix. Extensive experiments on three real-world datasets demonstrate the proposed method outperforms other baselines in the rating prediction task.

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.

DAN: a deep association neural network approach for personalization recommendation

The collaborative filtering technology used in traditional recommendation systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional recommendation algorithms, thus leading to the emergence of recommendation systems based on deep learning. At present, deep learning recommendations mostly use deep neural networks to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep neural network recommendation algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the recommendation. Aimed at this problem, in this paper we propose a feedforward deep neural network recommendation method, called the deep association neural network (DAN), which is based on the joint action of multiple categories of information, for implicit feedback recommendation. Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the recommendation is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint recommendations can provide better recommendation performance.

Algorithm Optimization for Cold Start of Collaborative Filtering System

With the development of network technology, the amount of information on the network has grown and expanded rapidly with an exponential law, and its information organization is heterogeneous, diverse, and distributed. With the vigorous development of Internet information services, the scale of its information resources has also exploded. For ordinary users, the problems of “information trek” and “information overload” on the Internet are becoming increasingly serious. In order to solve the problem of information overload, recommendation system has become an indispensable tool for today’s e-commerce platform, which can help users find valuable information quickly. Collaborative filtering-based recommendation algorithms have been widely applied and studied in recommendation systems. Although the collaborative filtering algorithm has been widely used, there are still problems such as data sparsity, scalability, and cold start, which seriously limit the quality of recommendations. Therefore, collaborative filtering algorithms face many challenges. Especially recommendation systems using collaborative filtering technology. This article discusses the cold start system, the cold start user, and the cold start scenario. Effective use of project content information and user personal information is one of the effective methods to solve the cold start problem, that is, a hybrid recommendation technology combining information filtering and collaborative filtering. This paper proposes a hybrid recommendation technology that can better solve the cold start problem.

A Hybrid Two-Phase Recommendation for Group-Buying E-commerce Applications

The last two decades have witnessed an explosive growth of e-commerce applications. Existing online recommendation systems for e-commerce applications, particularly group-buying applications, suffer from scalability and data sparsity problems when confronted with exponentially increasing large-scale data. This leads to a poor recommendation effect of traditional collaborative filtering (CF) methods in group-buying applications. In order to address this challenge, this paper proposes a hybrid two-phase recommendation (HTPR) method which consists of offline preparation and online recommendation, combining clustering and collaborative filtering techniques. The user-item category tendency matrix is constructed after clustering items, and then users are clustered to facilitate personalized recommendation where items are generated by collaborative filtering technology. In addition, a parallelized strategy was developed to optimize the recommendation process. Extensive experiments on a real-world dataset were conducted by comparing HTPR with other three recommendation methods: traditional CF, user-clustering based CF, and item-clustering based CF. The experimental results show that the proposed HTPR method is effective and can improve the accuracy of online recommendation systems for group-buying applications.

An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy

The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.

Neural-network based adaptive context prediction model for ambient intelligence

To overcome the limitations of the conventional medical service in terms of ageing and chronic diseases, AmI-based precision medicine has drawn particular attention. Precision medicine is a customized personal medical service using various information technologies such as personal health device, AI algorithm, image recognition, voice recognition, and natural language processing. In particular, the information technologies for follow-up care services for patients, such as context awareness, context information, and inference rules, are required. In PHD, contexts such as variable data include blood pressure, BMI, blood sugar, weather, and food. It has time-series characteristics, meaning that it changes often with time. Other kinds of health-related information, such as age, family history, smoking, and residential area, are intermittently changed. For inference that is highly related to a user, the context collected through AmI is presented with ontology. Ontology consists of a user’s ambient data, weather data, and lifelog. Context is changed along with a user’s ambient conditions and time. An inference engine is used to create the knowledge base and predict a change. This study proposes a neural-network based adaptive context prediction model for ambient intelligence. This is a learning model using neural network to calculate the similarity for recommendation in a mining lifecare platform. In a conventional prediction procedure, an error is used to update a weight. The proposed model learns the similarity weight of the users to become adapted to the user’s ambient. Based on the knowledge base, user clustering and deviation from mean are applied to calculate the similarity weight. Collaborative filtering technology is used to predict a user’s context and learn the similarity weight repeatedly using a neural network. According to the performance evaluation, the proposed neural-network based similarity weight method had the highest accuracy of prediction when the learning rate was 0.001. Consequently, we found that AmI is a new added-value technology to maintain a healthy lifestyle and contributes to developing the healthcare industry and improving the quality of life.

Design of Digital Network Shared Learning Platform Based on SCORM Standard

Aiming at the problem of low utilization rate of teaching resources, a personalized recommendation system of digital teaching resources under SCORM standard was put forward. Based on the learner's learning process, standards and personalized recommendation techniques, a SCORM digital teaching resource management model was constructed. According to the rec-ommendation algorithm of collaborative filtering in personalized recommendation technology, a digital teaching resource recommendation model based on SCORM standard was constructed. The SCORM digital teaching resource library system based on collaborative filtering technology was designed and implemented. The system was evaluated. The data of the assessment were analyzed. The results showed that the recommendation system had a good promoting effect on the learners. Therefore, the system can provide personalized recommendations for learners. It improves learning efficiency.

Design and Analysis of Efficient Collaborative Filtering Based Optimization Approach

The large amount of information that is currently being collected (the so-called “big data”), have resulted in model-based Collaborative Filtering (CF) methods to encountering limitations, e.g., the sparsity problem and the scalability problem. It is difficult for model-based CF methods to address the scalability-performance trade-off. Therefore, we propose a scalable clustering-based CF method in this paper that can help provide a balance by re-locating elements in the cluster model. The rapid development of information technology takes our shopping into the orbit of information. With the network construction of resources, the amount of shopping resources increases rapidly. Collaborative filtering (CF) is a technique commonly used to build personalized recommendations on the Web. Some popular websites that make use of the collaborative filtering technology include Amazon, Netflix, iTunes, IMDB. The most important issue which influences the collaborative filtering recommendation accuracy is the so-called data sparseness. Data sparseness causes the system difficulty in determining the nearest neighbors of the target user accurately. Clustering can solve this problem to some extent. Grouping a set of physical or abstract objects into classes of similar objects, this process is called as clustering. This paper presents the methods to generate recommendations using clustering-based collaborative filtering approach.

New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy

Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era. However, with the increase of the users’ information-security awareness, these data are reduced or the quality of the data becomes worse. Singular Value Decomposition (SVD) is one of the common matrix factorization methods used in collaborative filtering, which introduces the bias information of users and items and is realized by using algebraic feature extraction. The derivative model SVD++ of SVD achieves better predictive accuracy due to the addition of implicit feedback information. Differential privacy is defined very strictly and can be proved, which has become an effective measure to solve the problem of attackers indirectly deducing the personal privacy information by using background knowledge. In this paper, differential privacy is applied to the SVD++ model through three approaches: gradient perturbation, objective-function perturbation, and output perturbation. Through theoretical derivation and experimental verification, the new algorithms proposed can better protect the privacy of the original data on the basis of ensuring the predictive accuracy. In addition, an effective scheme is given that can measure the privacy protection strength and predictive accuracy, and a reasonable range for selection of the differential privacy parameter is provided.

A Recommendation Method basing on Synthetic Strategy for Agricultural Science and Technology Information

To ameliorate the limitations of traditional collaborative filtering technologies and enhance the recommendation quality of agricultural science and technology information, a collaborative filtering recommendation method based on synthetic strategy is proposed. Firstly, filter the user set and user-item rating matrix according to the location of target user, which can solve the regional problem. Then, predict ratings of items according to the similarity of users or item content, which can relax the impact of the sparse rating. In addition, add the rating time to the user-item rating matrix to distinguish the timeliness of the user preference, and add user preference shifting in the similarity formula as a factor which can express the similarity of users or item content better. Our method can not only guarantee the recommended information is local and suit to current season of agricultural production, but also ensure the recommending precision under sparse rating data.

The Research on Curling Track Empty Value Fill Algorithm Based on Similar Forecast

The sparsity problem could result in a data-dependent reduction and we couldn't do rough set null value estimates, therefore, we need to deal with the problem of a sparse data set before performing the null value estimate and padded by introducing a collaborative filtering technology used the sparse data processing methods - project- based score prediction in the study. The method in the case of the object attribute data sparse, two objects can be based on their known attributes of computing the similarity between them, so a target object can be predicted based on the similarity between the size of the other objects to the N objects determine a neighbor collection of objects and then treat the predicted target unknown property by neighbors object contains attribute values to predict.