Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems

Lucas, Joel Pinho; Segrera, Saddys; Moreno, María N.

doi:10.1016/j.eswa.2011.07.136

Cited by 35 publications

(10 citation statements)

References 20 publications

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“…The top priority task of TV company personnel is to control the ratio of programs of different genres when forming the broadcast grid in order to increase and maintain the rating of the channel [1]. Intelligent recommendation systems in the TV domain are based on the classification rules connecting the time factors and the users' preferences with the TV ratings [2,3]. An automatic recommendation scheme based on collaborative filtering infers the preferred TV programs in two stages [4].…”

Section: Discussionmentioning

confidence: 99%

“…The weekly TV program set was refined using the methods of genetic tuning applied to the linguistic models enhanced with SVD [3,7,20]; min-max neural networks [12]; SVM [14]. The given method simplifies the process of the program set refinement by solving the primary system of fuzzy relational equations.…”

Section: Effectiveness Estimation Of the Hybrid Approachmentioning

confidence: 99%

“…Candidate rule generation corresponds to the construction of a zero option of the timetable. In works [3,7,12,14,20], candidate rule generation requires solving the optimization problem with 2nZ variables for boundaries of interval rules. Application of the hybrid approach allows reducing the number of tuning parameters by solving Z optimization problems for 2T boundaries of significance measures [25][26][27].…”

Section: Effectiveness Estimation Of the Hybrid Approachmentioning

confidence: 99%

See 2 more Smart Citations

Television Rating Control in the Multichannel Environment Using Trend Fuzzy Knowledge Bases and Monitoring Results

Азаров

Krupelnitsky

Rakytyanska

2018

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The purpose of this study is to control the ratio of programs of different genres whenforming the broadcast grid in order to increase and maintain the rating of a channel. In themultichannel environment, television rating controls consist of selecting content, the ratings ofwhich are completely restored after advertising. The hybrid approach to rule set refinement basedon fuzzy relational calculus simplifies the process of expert recommendation systems construction.By analogy with the problem of the inverted pendulum control, the managerial actions aim to retainthe balance between the fuzzy demand and supply. The increase or decrease trends of the demandand supply are described by primary fuzzy relations. The rule-based solutions of fuzzy relationalequations connect significance measures of the primary fuzzy terms. Program set refinement bysolving fuzzy relational equations allows avoiding procedures of content-based selective filtering.The solution set generation corresponds to the granulation of television time, where each solutionrepresents the time slot and the granulated rating of the content. In automated media planning,generation of the weekly TV program in the form of the granular solution provides the decrease oftime needed for the programming of the channel broadcast grid.

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Section: Discussionmentioning

confidence: 99%

Section: Effectiveness Estimation Of the Hybrid Approachmentioning

confidence: 99%

Section: Effectiveness Estimation Of the Hybrid Approachmentioning

confidence: 99%

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Television Rating Control in the Multichannel Environment Using Trend Fuzzy Knowledge Bases and Monitoring Results

Азаров

Krupelnitsky

Rakytyanska

2018

Data

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“…The user behavior can be understood from the way in which the user responds to other items. Content-based filtering techniques [5] can be connected to the information given by the client certainly or expressly. The data is created in view of this information given by the client.…”

Section: IImentioning

confidence: 99%

“…Ontology overview SWOOP[5] is a Web Ontology Language editor tool. This provides a platform to compare, edited and merge several ontologies.…”

mentioning

confidence: 99%

Web Revisitation and Personalized Content Retrieval using Modular Ontology

Nisha¹

2018

IJRASET

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Getting back to antecedently viewed sites may be a common nonetheless uneasy task for users as a result of the massive volume of in-person accessed info on the online. This technique leverages human's natural recall method exploitation episodic and long-term memory cues to facilitate recall and presents a private net revisitation technique through context and content keywords. Underlying techniques for context and content reminiscences acquisition, storage, decay, associate degreed utilization for page re-finding provides the user with tailored and personalized services includes a preponderating importance once developing an interactive system. personalized retrieval data is a very important drawback moon-faced. It usually depends on the user's preference and demand. However, anticipating user's interest remains another challenge to be overcome. this can be associated with one hand, to the shortage of specific process modeling for information discovery, and on another hand, to fitting properly with what the user needs. a replacement technique supported user profile ontology designed through ontology modularization is proposed. The discovering of such preferences is achieved by a brand new methodology of content-based ontology that aims to require into thought each the hierarchical and also the non-hierarchical (semantic) relations put together with connected properties. Three modules are presented. first is context acquisition module, the second is employed to keeping specific user's preferences delineated by these rules and therefore the third module permits for structuring the expanding rule

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Active Learning and Deep Learning for the Cold-Start Problem in Recommendation System: A Comparative Study

Alabdulrahman

Viktor

Paquet

2020

Communications in Computer and Information Science

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Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems

Cited by 35 publications

References 20 publications

Television Rating Control in the Multichannel Environment Using Trend Fuzzy Knowledge Bases and Monitoring Results

Television Rating Control in the Multichannel Environment Using Trend Fuzzy Knowledge Bases and Monitoring Results

Web Revisitation and Personalized Content Retrieval using Modular Ontology

Active Learning and Deep Learning for the Cold-Start Problem in Recommendation System: A Comparative Study

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