RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment

Alhamid, Mohammed F.; Rawashdeh, Majdi; Dong, Hao; Hossain, M. Shamim; Alelaiwi, Abdulhameed; Saddik, Abdulmotaleb El

doi:10.1007/s00530-015-0469-2

Cited by 21 publications

(7 citation statements)

References 30 publications

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“…"Supervised learning" and "Similarity-based"). Most studies that used contextual ontologies also adopted some semantic-based algorithm, thus, the combination of semantic-based and context-awareness techniques is present in many studies found in this LR (Naudet and Schwartz, 2010 (Alhamid et al, 2015). 5.…”

Section: Context-awareness Algorithmsmentioning

confidence: 96%

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A literature review of recommender systems in the television domain

Véras

Prota

Bispo

et al. 2015

Expert Systems with Applications

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Section: Context-awareness Algorithmsmentioning

confidence: 96%

“…Finally, it is important to mention that only thirteen studies had more than one recommendation type: (Cremonesi et (Alhamid et al, 2015). For instance, study (Rey-López et al, 2010) recommends Education and Commercial item types.…”

Section: Recommendation Itemsmentioning

confidence: 97%

A literature review of recommender systems in the television domain

Véras

Prota

Bispo

et al. 2015

Expert Systems with Applications

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“…However, most of them only take into account the context in which the group receiving the recommendations is located. There are some exceptions to this, as in [31], where a contextaware framework for multimedia recommendations is presented. It obtains information from intelligent environmental interfaces (location, emotions, physical conditions, etc.)…”

Section: Context-aware Recommender Systemsmentioning

confidence: 99%

Context-Aware Music Recommender Systems for Groups: A Comparative Study

2021

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Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.

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“…AmI is an electronic environment capable of sensing and responding the presence of people with the help of some set of promising technological approaches. An interactive and intelligent framework has been proposed in [77], which combines sensors with Ambient Intelligent to implicitly provide inputs to the RS systems to enhance users' satisfaction. The framework also has the ability to personalize recommendations for the group of users instead of individual users.…”

Section: (3) Multimedia Domainmentioning

confidence: 99%

“…[37,50,70,71,75,77,78,90,92,100,[103][104][105][106][107]109,111,128,130,133,136,137,160,161,164,165] f-measure To measure the harmonic mean of recall and precision…”

Section: Precisionmentioning

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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RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment

Cited by 21 publications

References 30 publications

A literature review of recommender systems in the television domain

A literature review of recommender systems in the television domain

Context-Aware Music Recommender Systems for Groups: A Comparative Study

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

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