Knowledge graph-based multi-context-aware recommendation algorithm

Wu, Chao; Liu, Sanya; Zeng, Zeyu; Mao, Chen; Alhudhaif, Adi; Tang, Xiangyang; Alenezi, Fayadh; Alnaim, Norah; Peng, Xicheng

doi:10.1016/j.ins.2022.02.054

Cited by 48 publications

(14 citation statements)

References 29 publications

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“…Furthermore, it is also essential to build an adaptive recommendation model of online ideological as well as the political teaching resources, and adopt the methods of information fusion and big data clustering analysis [7]. Build a mining model of online ideological as well as the political teaching resources' interest preference characteristics of online ideological as well as the political teaching audience [8]. Under the heterogeneous storage structure mode, adopt regional fusion method to carry out dynamic structure reorganization in the process of online ideological as well as the political teaching resources' selfadaptive recommendation, and adopt personalized information fusion method to build the recommendation model.…”

Section: Data Distribution Of Adaptive Recommendation Of Onlinementioning

confidence: 99%

A Self-Adaptive Recommendation Method for Online Ideological and Political Teaching Resources Based on Deep Reinforcement Learning

Jin

2022

Mobile Information Systems

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The online ideological as well as the political teaching resource management system structure is established in the view of information management in colleges and universities. Furthermore, the online ideological as well as the political teaching information level is improved by combining the optimized design of resource recommendation model. In this paper, an online ideological as well as political teaching resource adaptive recommendation system and algorithm, which is designed on deep reinforcement learning, is suggested. The cost relationship model between online ideological as well as the political teaching resources and learning profitability is constructed. Similarly, the multidimensional constraint index parameter analysis method is adopted, and the adaptive matching model of online ideological as well as the political teaching resources is established. According to online ideological as well as the political teaching norms, combined with the analysis of high-quality educational resources of audience groups, the dynamic evaluation of online ideological as well as the political teaching resources and the adaptive matching model of interest preferences are established. Finally, the deep reinforcement learning method is adopted. By analyzing the characteristics of the resource structure model of online ideological as well as the political teaching resources, through benefit evaluation, resource supply and demand balance management analysis and balanced game control, the online ideological as well as the political teaching resources management system can be improved and self-adaptive recommended. The simulation outcomes indicate that this approach has noble adaptability and high correctness in recommending online ideological as well as the political teaching resources.

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Section: Data Distribution Of Adaptive Recommendation Of Onlinementioning

confidence: 99%

A Self-Adaptive Recommendation Method for Online Ideological and Political Teaching Resources Based on Deep Reinforcement Learning

Jin

2022

Mobile Information Systems

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“…This study selected the Meclass dataset as the experimental basis, with a total of 281 users and 1268 resources [24]. There were a total of 30000 rating data, with a training to test ratio of 8:2.…”

Section: B Performance Verification Analysis Of Hybrid Recommendation...mentioning

confidence: 99%

Intelligent Personalized Recommendation Method Based on Optimized Collaborative Filtering Algorithm in Primary and Secondary Education Resource System

Feixiang

2024

IEEE Access

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The Internet has driven the development of online education, and the vast system of educational resources has put forward higher requirements for personalized recommendation systems. In response to this issue, this study proposes a personalized recommendation system on the ground of optimized collaborative filtering algorithms. Due to the strong interaction between collaborative filtering algorithms and users, they are often used in recommendation models. However, its defects such as cold start can weaken the performance of the model. This study introduces content recommendation algorithms to address this phenomenon. A hybrid recommendation model on the ground of the two algorithms can effectively achieve personalized recommendations. Meanwhile, this study focuses on the key modules in the overall model and utilizes standardization and dimensionality reduction operations to further reduce the computational burden on the system. Finally, to verify the reliability of the model, the study compared it with other models. The experimental results showed that the accuracy of the mixed recommendation model was 2.68% higher than that of the utility recommendation model and the rule recommendation model, respectively, and 7.99%. Therefore, the personalized recommendation model on the ground of optimized collaborative filtering algorithm proposed in the study is effective.

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“…Each pairwise aggregation is an aggregation operator that is applied to the similarity values. Some examples of aggregation operators are the arithmetic mean and the OWA operator [27].…”

Section: Feedback-driven Automatic Consensus Support Modelmentioning

confidence: 99%

Reaching consensus in group recommendation systems

Gorbatenko,

Hodovychenko

2024

HAIT

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Conventional group recommender systems fail to take into account the impact of group dynamics on group recommendations, such as the process of reconciling individual preferences during collective decision-making. This scenario has been previously examined in the context of group decision making, specifically in relation to consensus reaching procedures. In such processes, experts engage in negotiations to determine their preferences and ultimately pick a mutually agreed upon option. The objective of the consensus procedure is to prevent dissatisfaction among group members about the suggestion. Prior studies have tried to accomplish this characteristic in group recommendation by using the minimal operator for the process of aggregating recommendations. Nevertheless, the use of this operator ensures just a minimal degree of consensus on the proposal, but it does not provide a satisfactory level of agreement among group members over the group recommendation. This paper focuses on analyzing consensus reaching procedures in the context of group recommendation for group decision making. The goal of the study is to use consensus reaching processes to provide group recommendations that satisfy all members of the group. Additionally, study aims to enhance group recommender systems by ensuring an acceptable level of agreement among users regarding the group recommendation. Therefore, group recommender systems are expanded by including consensus reaching mechanisms to facilitate group decision making. In the context of group decision making, a collective resolution is reached by a group of persons, who may be specialists, from a pool of options or potential solutions to the issue at hand. To do this, each specialist obtains their preferences about each possibility. The conventional selection techniques for group decision-making difficulties fail to include the possibility of dissent among experts over the chosen choice. This issue is alleviated by using consensus-building techniques, in which a substantial degree of agreement is attained prior to picking the ultimate decision. To facilitate alignment of experts' tastes, they repeatedly modify them to increase their proximity. Prior to making collective choices, it is sometimes necessary to establish a certain degree of consensus. Thus, this paper presents a group recommendation architecture that utilizes automated consensus reaching models to provide accepted suggestions. More specifically, we are considering the minimal cost consensus model and the automated consensus support system model that relies on input. The minimal cost consensus model calculates the collective suggestion of a group by adjusting individual preferences based on a cost function. This is achieved via the use of linear programming. The feedback-based automated consensus support system model mimics the interaction between group members and a moderator. The moderator offers adjustments to individual suggestions in order to bring them closer together and achieve a high degree of agreement before generating the group recommendation. Both models are assessed and contrasted with baseline procedures in the testing.

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Knowledge graph-based multi-context-aware recommendation algorithm

Cited by 48 publications

References 29 publications

A Self-Adaptive Recommendation Method for Online Ideological and Political Teaching Resources Based on Deep Reinforcement Learning

A Self-Adaptive Recommendation Method for Online Ideological and Political Teaching Resources Based on Deep Reinforcement Learning

Intelligent Personalized Recommendation Method Based on Optimized Collaborative Filtering Algorithm in Primary and Secondary Education Resource System

Reaching consensus in group recommendation systems

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