Online Learning in Large-Scale Contextual Recommender Systems

Song, Linqi; Tekin, Cem; Schaar, Mihaela van der

doi:10.1109/tsc.2014.2365795

Cited by 99 publications

(53 citation statements)

References 27 publications

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“…Eventually, a recommendation strategy should be able to provide users with relevant information depending on the context [20,9,21] (i.e. user location, observed items, etc.)…”

Section: Related Workmentioning

confidence: 99%

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SOS: A multimedia recommender System for Online Social networks

Amato

Moscato

Picariello

et al. 2019

Future Generation Computer Systems

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“…Eventually, a recommendation strategy should be able to provide users with relevant information depending on the context [20,9,21] (i.e. user location, observed items, etc.)…”

Section: Related Workmentioning

confidence: 99%

“…Finally, a category of recommender systems, named Large Scale Recommender Systems [21], calls for new capabilities of such applications to deal with very large amount of data with respect to scalability and efficiency issues.…”

Section: Related Workmentioning

confidence: 99%

SOS: A multimedia recommender System for Online Social networks

Amato

Moscato

Picariello

et al. 2019

Future Generation Computer Systems

View full text Add to dashboard Cite

“…To be precise, the authors of [68] claimed to proposed a scalable large-scale context-aware recommender system that does not suffer from cold start problems. The approach uses an adaptive item clustering algorithm to address the cold start problem and improve the learning speed.…”

Section: (2) E-documents Domainmentioning

confidence: 99%

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

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Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.

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“…It written in python having efficient data mining and data analytics capability for the implementation of the system. H. "Online Learning in Large-scale Contextual Recommender Systems" [1]et al The paper, propose a contextual MAB based clustering approach to design and deploy recommender systems for a large number of users and items, while taking into consideration the context in which the recommendation is made. Our proposed ACR algorithm makes use of an adaptive item clustering method to improve the learning speed.…”

Section: Literature Survey and Observations About Usage In Proposmentioning

confidence: 99%

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2018

IJCSE

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Abstract:Using a CSA system, the student will be suggested with the list of colleges having the noncentralized courses and according to his marks entered in the system, appropriate course will be suggested to him. Firstly, the student must enter his marks and based on his performance the CSA system will suggest the course and the college. The student will make a choice about the course and the admission process about the same will be conducted. All the admission process like the college cutoff, vacancies will be checked as well as the fees payment and the admit card procedure will be done.

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Online Learning in Large-Scale Contextual Recommender Systems

Cited by 99 publications

References 27 publications

SOS: A multimedia recommender System for Online Social networks

SOS: A multimedia recommender System for Online Social networks

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

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