Workshop on Context-Aware Recommender Systems

Adomavicius, Gediminas; Bauman, Konstantin; Mobasher, Bamshad; Ricci, Francesco; Tuzhilin, Alexander; Unger, Moshe

doi:10.1145/3383313.3411533

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…At that period of time, the phrase "Recommender System" emerged. These systems can be considered an efficient mechanism for refining data and filtering information (Adomavicius et al, 2020).…”

Section: History Of Recommender Systemsmentioning

confidence: 99%

“…The social networks' expansion and the increase in the information contained in them, such as likes, comments, friends, followers, the following, and tags, has created a rich source of information for researchers (Adomavicius et al, 2020).…”

Section: Hybrid Filtering Recommender Systemsmentioning

confidence: 99%

“…In this environment, there are various devices and sensors that collect information about the user's context. Such information can be used in recommender systems to form a new generation of systems known as Context-Aware Filtering (Adomavicius et al, 2020).…”

Section: Hybrid Filtering Recommender Systemsmentioning

confidence: 99%

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A hybrid semantic recommender system based on an improved clustering

Bahrani,

Minaei-Bidgoli,

Parvin

et al. 2024

J Supercomput

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A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the clients/people/consumers/users according to their (previous) interests. These systems are expected to recommend the items according to the users' interests. There are two traditional general recommender system models, i.e., Collaborative Filtering Recommender System (ColFRS) and Content-based Filtering Recommender System (ConFRS). Also, there is another model that is a hybrid of those two traditional recommender systems; it is called Hybrid Recommender System (HRS). An HRS usually outperforms simple traditional recommender systems. The problems such as scalability, cold start, and sparsity belong to the main problems that any recommender system should deal with. The memory-based (modeless) recommender systems benefit from good accuracies. But they suffer from a lack of admissible scalability. The model-based recommender systems suffer from a lack of admissible accuracies. But they benefit from good scalability. In this paper, it is tried to propose a hybrid model based on an automatically improved ontology to deal with the scalability, cold start, and sparsity problems. Our proposed HRS also uses an innovative approach of clustering as an augmented section. When there are enough ratings, it uses a collaborative filtering approach to predict the missing ratings. When there are not enough ratings, it uses a content-based filtering approach to predict the missing ratings. In the content-based filtering section of our proposed HRS, ontology concepts are used to improve the accuracy of ratings' prediction. If our target client is severely sparse, we can not trust even the ratings predicted by the content-based filtering section of our proposed HRS. Therefore, our proposed HRS uses additive clustering to improve the prediction of the missing ratings if the target client is severely sparse. It is experimentally shown that our model outperforms many of the newly developed recommender systems.

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Section: History Of Recommender Systemsmentioning

confidence: 99%

Section: Hybrid Filtering Recommender Systemsmentioning

confidence: 99%

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A hybrid semantic recommender system based on an improved clustering

Bahrani,

Minaei-Bidgoli,

Parvin

et al. 2024

J Supercomput

View full text Add to dashboard Cite

show abstract

“…The integration of contextual information into recommender systems to improve their performance has recently received considerable attention in the research literature [15][16][17][18][19][20][21]. Studies in the relevant literature can be classified into three paradigms depending on which component of the recommendation process the contextual information is included in: contextual pre-filtering, contextual post-filtering, and contextual modeling.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems

VU¹,

Le²

2023

Computer Systems Science and Engineering

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Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users' demands based on their preference profiles. Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems. CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations. However, the majority of CARSs use ratings as a unique criterion for building communities. Meanwhile, MCRSs utilize user preferences in multiple criteria to better generate recommendations. Up to now, how to exploit context in MCRSs is still an open issue. This paper proposes a novel approach, which relies on deep learning for context-aware multi-criteria recommender systems. We apply deep neural network (DNN) models to predict the context-aware multi-criteria ratings and learn the aggregation function. We conduct experiments to evaluate the effect of this approach on the real-world dataset. A significant result is that our method outperforms other state-of-the-art methods for recommendation effectiveness.

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“…LocalRec [2] focused on incorporating user location in recommendation. CARS [1] also attracts researchers that aim to incorporate various contextual information into recommendation, including location information. None of the workshops, however, focus on cross-domain item recommendation.…”

Section: Prior Workhops On the Topicmentioning

confidence: 99%