Lazy collaborative filtering with dynamic neighborhoods

Suganeshwari, G.; Ibrahim, Syed; Li, Gang

doi:10.1108/idd-02-2018-0007

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…In recent years, the rapid growth of online platforms and the amount of information available has become a major challenge for users in finding relevant and personalized content [1]- [3]. Recommender systems are emerging as a solution to address this problem by providing suggestions on items that are likely to be of interest to users [4].…”

Section: Introductionmentioning

confidence: 99%

Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering

Widayanti

2023

J. Appl. Data Sci.

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This innovative study introduces a novel enhancement to recommendation systems through a synergistic integration of Collaborative Filtering (CF) and Content-Based Filtering (CBF) techniques, termed the hybrid CF-CBF approach. By seamlessly amalgamating the strengths of CF's user interaction insights and CBF's content analysis prowess, this approach pioneers a more refined and personalized recommendation paradigm. The research encompassed meticulous phases, including comprehensive data acquisition, efficient storage management, meticulous data refinement, and the skillful application of CF and CBF methodologies. The findings markedly highlight the prowess of the hybrid approach in generating recommendations that exhibit enhanced diversity and precision, surpassing the outcomes obtained from either technique in isolation. Remarkably, the hybrid CF-CBF approach effectively addresses the inherent shortcomings of individual methods, such as CF's vulnerability to the "cold start" problem and CBF's limitation in fostering recommendation diversity. By fostering a harmonious synergy, this novel approach transcends these limitations and provides a holistic solution. Furthermore, the interplay of CF and CBF augments the recommender system's cognitive grasp of user preferences, subsequently enriching the quality of recommendations provided. In conclusion, this research stands as a pioneering contribution to the evolution of recommendation systems by championing the hybrid CF-CBF approach. By ingeniously fusing two distinct techniques, the study engenders a breakthrough in personalized recommendations, thereby propelling the advancement of more sophisticated and effective recommendation systems.

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Section: Introductionmentioning

confidence: 99%

Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering

Widayanti

2023

J. Appl. Data Sci.

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“…With the development of pedagogical data, the need to find productive information has emerged as a hot topic among the research community [1]. Many data mining techniques have been exploited in this direction to achieve better insight of different academic data warehouses [2,3].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble and Filtering-Based System for Predicting Educational Data Mining

Hananto¹

2021

J. Appl. Data Sci.

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When developing a prediction paradigm, an ensemble technique such as boosting is used. It is built on a heuristic framework. Generally speaking, engineering ensemble learning is more accurate than individual classifiers when it comes to making predictions. Consequently, numerous ensemble strategies have been presented in this work, particularly to provide a more complete understanding of the essential methods in general. Researchers have experimented with boosting methods to forecast student performance as part of a variety of ensemble techniques. The researchers employed improvement approaches to construct an accurate predictive educational model, which was based on a key phenomena seen in categorization and prediction operations. In light of the uniqueness and originality of the suggested strategy in educational data mining, the researchers used augmentation strategies in order to construct an accurate predictive pedagogical model. Tenfold cross-validation was performed to evaluate the effectiveness of the basic classifiers, which included the random tree, the j48, the knn, and the Naive Bayes. The random tree was found to be the most effective classifier. Several additional screening techniques, including oversampling (SMOTE) and undersampling (Spread subsampling), were utilized to analyze any statistically significant variations in results between the meta and base classifiers that had been identified between the meta and base classifiers. The use of ensemble and screening strategies, as compared to the use of standard classifiers, has demonstrated considerable gains in predicting student performance, as has the use of either strategy alone. Furthermore, after the completion of a performance research on each approach, two new prediction models have been established on the basis of the improved results gained thus far.

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