Solving distribution problems in content-based recommendation system with gaussian mixture model

Dat, Nguyen Van; Toan, Pham Van; Thanh, Ta Minh

doi:10.1007/s10489-021-02429-9

Cited by 22 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The test results show that the recommended accuracy of the model is significantly higher than that of the comparison model. In practical application projects, the response time is shorter, the calculation speed is increased by 24.37% on average compared with the four comparison models, and the calculation results are the most stable and reliable [12].…”

Section: Related Workmentioning

confidence: 98%

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

Zheng,

2024

SCPE

View full text Add to dashboard Cite

With the development and maturity of the Internet education industry, more and more vocational colleges have opened English teaching courses based on massive open online courses courses. However, there are many English-related courses on the massive open online courses course platform, and the use of scientific recommendation models can improve the teaching quality of such courses. Therefore, this research attempts to design two improved attention mechanisms and user-based embedded expression using meta-path technology. At the same time, these two are combined with reinforcement learning technology to design an improved massive open online courses English course recommendation model. The test results show that the hit rate of the model designed in this study is 89.84%, 74.28%, 70.81% and 71.35% respectively when the rank number is 20 and the parameter is 10. At this time, the cumulative income of normalized discount is 48.24%, 34.58%, 25.96% and 28.69% respectively. However, when the number of calculated samples reaches the maximum value of 1158609, the calculation time of the improved reinforcement learning recommendation model is 1867 seconds, which is also higher than the comparison model. The experimental results show that the curriculum recommendation accuracy of the massive open online courses recommendation model designed in this study is higher and the recommendation results are more reasonable. The results of this research have a certain application potential in the field of the construction of online education in colleges and universities.

show abstract

Section: Related Workmentioning

confidence: 98%

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

Zheng,

2024

SCPE

View full text Add to dashboard Cite

show abstract

“…Traditional recommendation systems, i.e. 2D recommendation systems, such as content-based recommendation systems [2] and collaborative-based recommendation systems [3] analyze the interactions between users and items through the user's ratings. Existing RSs suffer from three main problems 1) inaccurate personalized recommendations tailored to the user's behaviour, 2) data sparsity, and 3) scalability for largescale datasets.…”

Section: List Of Tablesmentioning

confidence: 99%

MCARS-CC: A Scalable Multi-context-aware Recommender System

Nawara

2024

Preprint

View full text Add to dashboard Cite

<p>Recommendation Systems (RSs) have proved a compelling performance to overcome the data overload problem. Context-aware recommenders guide users/clients to more personalized recommendations. Incorporating contextual features in recommendation systems improves the systems' accuracy; however, they still suffer from sparsity and scalability problems which impact the quality of recommendations. To overcome these limitations, we propose a multi-context-aware recommendation system using the notion of consensus clustering, named MCARS-CC. Experimental results on real-world datasets show that the concept of consensus learning using clustering analysis can significantly improve the recommender systems' accuracy, handle sparsity in data, and address scalability for large-scale datasets. The proposed method surpasses the other recommendation algorithms in terms of accuracy, precision and recall. The MAE and RMSE results show that consensus clustering leads to better accuracy measures and a more stable resilient recommendation system; in particular, incorporating Hyber-Graph with Partitioning (HGPA) Consensus has improved MAE and RMSE by 25.96% and 8.94%, respectively, in Yelp-Dataset.</p>

show abstract

“…When choosing a pair of shoes, we do not evaluate each pair of shoes independently. Alternatively, we are inclined to evaluate and then choose the desirable option [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Reaching consensus in group recommendation systems

Gorbatenko,

Hodovychenko

2024

HAIT

View full text Add to dashboard Cite

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.

show abstract

Solving distribution problems in content-based recommendation system with gaussian mixture model

Cited by 22 publications

References 25 publications

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

MCARS-CC: A Scalable Multi-context-aware Recommender System

Reaching consensus in group recommendation systems

Contact Info

Product

Resources

About