A comprehensive analysis on movie recommendation system employing collaborative filtering

Thakker, Urvish; Patel, Ripal; Shah, Manan

doi:10.1007/s11042-021-10965-2

Cited by 35 publications

(12 citation statements)

References 105 publications

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“…In practice, if only one's own preferences are considered, the recommendation results lack a certain degree of richness, and it is necessary to explore the potential preferences of users to improve user satisfaction [18]. The Potential Preference Model (PPM) is built by extracting the user's browsing history to form a matrix of user behavior and finding nearest neighbors through a mixture of behavioral and content similarity calculations to solve the problem of not being able to categories similar users due to text diversity [19]. When using CF for recommendation, it is transformed to recommend the feature words of interest to the nearest neighbors, which can effectively avoid the cold start problem, and the construction process is shown in Fig.…”

Section: B Hybrid Recommendation Algorithm With Fused Preference Modelsmentioning

confidence: 99%

Design of a Hybrid Recommendation Algorithm based on Multi-objective Collaborative Filtering for Massive Cloud Data

Zhou¹

2023

IJACSA

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The current recommendation technology has some problems, such as lack of timeliness, the contradiction between recommendation diversity and accuracy. In order to solve the problem of lack of timeliness, the time factor is introduced when constructing the self-preference model. The cold start problem in the collaborative filtering algorithm is solved by the hybrid similarity calculation method, and the potential preference model is constructed. The two are fused to obtain a hybrid recommendation algorithm to improve the recommendation performance of the algorithm. For the problem of multi-objective contradiction, the NNIA algorithm is used to further optimize the candidate results of mixed recommendation, and the final recommendation list is obtained. Through verification experiments, the results show that the recall rate and accuracy of the fused preference model are better than those of the non-fused model, and the accuracy is 9.57% and 8.23% higher than that of SPM and PPM, and the recall rate is 9.97% and 7.65% higher, respectively. CBCF-NNIA algorithm has high accuracy and diversity of recommendation, and can provide users with rich and diverse text content to meet their own needs.

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Section: B Hybrid Recommendation Algorithm With Fused Preference Modelsmentioning

confidence: 99%

Design of a Hybrid Recommendation Algorithm based on Multi-objective Collaborative Filtering for Massive Cloud Data

Zhou¹

2023

IJACSA

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“…Different e-learners must be linked to their sessions or transactions, online content data, pro les, results data, assigned task data, and other relevant data stored in the database throughout this stage. Thakker et al [21] presented in this paper the research movie recommender systems. Websites must provide individualized services to each user to increase customer happiness and the quality of the customer's time engagement when a user has a wide range of service options.…”

Section: Related Workmentioning

confidence: 99%

A Recommendation System Involving a Hybrid Approach of Student Review and Rating for an Educational Video

Hazar

Zrigui

2023

Preprint

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In face-to-face learning, lecturers directly manage their students and use all their emotional expressions to assess their understanding of courses. Contrastingly, in e-learning, online platforms suffer from the absence of emotional communication. Therefore, this paper addresses educational videos because they are the top closed type of delivered data that can express and clarify details about a subject's meaning. This paper's authors propose an educational video recommendation system based on the hybridization of student historical data. The system will recommend educational videos on learning platforms (the Coursera platform is a case study). We use latent Dirichlet allocation (LDA) for probabilistic topic models. LDA extracts topics from online video content. Simultaneously, we use sentiment analysis of learners' comments on educational videos to make new rating predictions and then hybrid the original user-rating matrix and predicted rating matrix to obtain a better recommendation.

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“…Many variables added to this issue, for example, change in atmospheric condition, advancement of microbe and vector as well as expansion in human versatility [16]. A portion of the normal infections that generally influence plants incorporates aster yellows, bacterial wither, scourge, rice bacterial curse, ulcer, crown nerve, decay, basal decay, and scab [17]. Ebb and flow works that address the ID and arrangement of plant illnesses utilizing different ML and DL strategies are introduced in the accompanying segment.…”

Section: Literature Surveymentioning

confidence: 99%

Apply Machine Learning with Smartphones to Detect and Recommend the Right Treatment for Plant Diseases

PritiKumari¹

2022

MSEA

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Plant sicknesses cause significant harvest creation misfortunes around the world, and a great deal of huge exploration exertion has been coordinated toward making plant infection distinguishing proof and therapy methods more viable. It would be of extraordinary advantage to ranchers to have the option to use the ongoing innovation to use the difficulties confronting horticultural creation and thus further develop crop creation and activity productivity. In this work, we planned and executed an easy-to-understand cell phone-based plant illness discovery and treatment suggestion framework utilizing AI (ML) methods. CNN was utilized for include extraction while the ANN and KNN were utilized to order the plant infections; a substance-based sifting proposal calculation was utilized to recommend significant medicines for the recognized plant illnesses after grouping. The aftereffect of the execution shows that the framework accurately identified and suggested treatment for plant illnesses.

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A comprehensive analysis on movie recommendation system employing collaborative filtering

Cited by 35 publications

References 105 publications

Design of a Hybrid Recommendation Algorithm based on Multi-objective Collaborative Filtering for Massive Cloud Data

Design of a Hybrid Recommendation Algorithm based on Multi-objective Collaborative Filtering for Massive Cloud Data

A Recommendation System Involving a Hybrid Approach of Student Review and Rating for an Educational Video

Apply Machine Learning with Smartphones to Detect and Recommend the Right Treatment for Plant Diseases

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