ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites

Tang, Yayuan; Guo, Ke; Zhang, Ruifang; Xu, Tao; Ma, Jun; Chi, Tao

doi:10.1007/s11280-019-00693-x

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…For example, Amazon's Today's Recommendations module can recommend some new items on the shelves to users based on the current popular products, users' recent purchase records, and browsing records, which can solve the cold start problem based on item recommendations [22][23][24]. e bundle sales module analyzes the user's purchase behavior through machine learning technology and recommends some items that are often bought together to the user.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Personalized Recommendation Model of High‐Quality Education Resources for College Students Based on Data Mining

Fang

Qiu-yun

2021

Complexity

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With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Personalized Recommendation Model of High‐Quality Education Resources for College Students Based on Data Mining

Fang

Qiu-yun

2021

Complexity

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“…In recent years, the recommendation system has been well developed and is playing an important role across social media. In the paper "ICFR: An Effective Incremental Collaborative Filtering based Recommendation Architecture for Personalized Websites" [23], the userbased collaborative filtering algorithm is applied in an incremental recommendation implementation method, in which three important elements: user, item, and rating, are redefined, and relationships between user preferences and recommended content are utilized to improve the user-based collaborative filtering algorithm. Moreover, users' browsing behaviors are extracted based on the analysis of Web logs from personalized websites, which can facilitate the update of users' historical preference in the design of an incremental algorithm.…”

Section: Behavior and Influence Analytics In Social Computingmentioning

confidence: 99%

Smart computing and cyber technology for cyberization

et al. 2020

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Following the well-known concepts of computerization and informatization, an emerging era of cyberization, which is considered as a reformation of the present physical, social and mental worlds, has become a hotly discussed trend in the new cyber world. Cyberization refers to using communication and computer technologies to interconnect computers and various electronic terminal devices distributed in different locations. It allows users to share software, hardware and data resources according to certain network protocols. Cyberization has greatly improved the practical utility of computers and has been widely applied in transportation, finance, business management, education, telecommunications, commerce, and so on in our daily life [1]. During the cyberization process, a large number of real things will conjugatively map to various kinds and levels of cyber existence in cyber world. It is said that cyberization has already taken place in a variety of fields along with the development of several emerging computing paradigms and information communication technologies, such as ubiquitous/ pervasive computing, social computing and networking, and wearable technologies, etc. Specifically, with the rapid growth of Internet of Things and cognitive cyber-physical systems, more and more digital things or cyber entities, are engaged or generated in the integrated cyber world. Emerging technologies in smart environments, such as smart computing and smart objects, become very significant, promising, and enabling issues in cyberization, to enhance

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“…It no longer requires users to describe their interests but builds an interest model for users based on the web pages they have visited. e Amazon.com book recommendation website system adopts collaborative filtering technology, which can analyze all users' purchases of books in a timely and accurate manner and then recommend books that have been purchased by other users who have purchased the same book to users [15,16]. e purchase history, products of interest, and other information including browsing products, topics of interest, demographic characteristics, and other information are combined together, and finally a list of books that users may buy (like) is displayed to users [17].…”

Section: Introductionmentioning

confidence: 99%

Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

Han

2020

Complexity

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Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user’s interest points according to the user’s behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error.

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ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites

Cited by 9 publications

References 29 publications

[Retracted] Personalized Recommendation Model of High‐Quality Education Resources for College Students Based on Data Mining

[Retracted] Personalized Recommendation Model of High‐Quality Education Resources for College Students Based on Data Mining

Smart computing and cyber technology for cyberization

Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

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