Towards context-sensitive collaborative media recommender system

Alhamid, Mohammed F.; Rawashdeh, Majdi; Osman, Hussein Al; Hossain, M. Shamim; Saddik, Abdulmotaleb El

doi:10.1007/s11042-014-2236-3

Cited by 61 publications

(30 citation statements)

References 31 publications

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“…Users are now overwhelmed with the huge quantity of social media resources and services. Authors of [74] proposed an RS model that utilizes these resources, which also incorporate rating information and social tags to personalize content search given a particular context. Their experimental results revealed the feasibility of personalized recommendations according to users' contexts and improving the cold start situations.…”

Section: (3) Multimedia 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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Section: (3) Multimedia Domainmentioning

confidence: 99%

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

et al. 2017

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“…A context-aware recommendation system model that utilizes social media resources is proposed by the work presented in [4] in which social tags as well as rating information are incorporated into the model to personalize the recommendation given a particular context. One of the best research we used as a benchmark to explore latent preference models for this work as well as for our previous work [29] is the work done by Alhamid et al [5].…”

Section: Related Workmentioning

confidence: 99%

A Clustering-Based Context-Aware Recommender Systems Through Extraction of Latent Preferences

Demissie

Shashi

2018

ijarcs

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Abstract:Recommender systems are tools that support personalization in terms of supporting navigation, sharing, and discovery of information and help users to find their desired content over the large volume of information. Recently, new research area on context-aware recommendations has emerged to provide the capability of utilizing social contents and exploit related tags and rating information and personalize the search for desired content by considering user's actual situation (contextual information). In this study, we propose an approach for clustering contextually similar information using unsupervised learning approach through K-Medoids clustering and demonstrate the extraction of latent preferences for recommending items under a given contextual cluster and study how such clusters of similar contextual information can be exploited to improve the prediction accuracy of a context-aware recommender systems. To evaluate the performance of our proposed recommendation strategy, the empirical analysis is conducted on the popular LDOS-CoMoDa dataset and we showed that our proposed approach outperforms state-of-the-art algorithms in terms of prediction accuracy of the computed recommendations.

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“…Alhamid et al [14] introduce a hybrid collaborative context approach that use a physiological data to enhance the recommendation process and show the importance of using contextual information for the improvement of the quality of recommender system. Abderrahim and Benslimane [15] propose a system for providing recommendation based on the Social Trust Network.…”

Section: Hybrid Filteringmentioning

confidence: 99%

Towards Semantics-Aware Recommender System: A LOD-Based Approach

Fridi¹,

Benslimane²

2017

IJMECS

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Abstract-Recommender systems have contributed to the success of personalized websites as they can automatically and efficiently select items or services adapted to the user's interest from huge datasets. However, these systems suffer of issues related to small number of evaluations; cold start system and data sparsity. Several approaches have been explored to find solutions to related issues. The advent of the Linked Open Data (LOD) initiative has spawned a wide range of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper, we aim to demonstrate that adding semantic information from LOD enhance the effectiveness of traditional collaborative filtering. To evaluate the accuracy of the semantic approach, experiments on standard benchmark dataset was conducted. The obtained results indicate that the accuracy and quality of the recommendation are improved compared with existing approaches.

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Towards context-sensitive collaborative media recommender system

Cited by 61 publications

References 31 publications

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

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

A Clustering-Based Context-Aware Recommender Systems Through Extraction of Latent Preferences

Towards Semantics-Aware Recommender System: A LOD-Based Approach

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