A Survey of Context-Aware Recommendation Schemes in Event-Based Social Networks

Xiao-mei, Huang; Liao, Guoqiong; Xiong, Naixue; Vasilakos, Athanasios V.; Lan, Tianming

doi:10.3390/electronics9101583

Cited by 12 publications

(6 citation statements)

References 93 publications

(142 reference statements)

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“…In [25] and [22], they focused only on the algorithms and modeling techniques used in context-aware recommenders. An application-focused survey was introduced in [23], where context-aware recommenders are presented only in an event-based social network-dependent platform. Our paper aims to provide a comprehensive overview across all dimensions of contextaware recommenders in the IoT environment, including the modeling, filtering, applications, evaluation, and limitations.…”

Section: Discussionmentioning

confidence: 99%

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Nawara

Kashef

2021

IEEE Access

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An essential goal of recommendation systems is to provide users with accurate and personalized recommendations that meet their preferences. With the rapid growth of IoT-connected sensors, the availability of contextual information has increased, and this has necessitated the fast development of Context-Aware Recommendation Systems (CARS). Context-Aware recommenders are different from traditional recommenders because of their ability to predict the ratings of target users/items by exploiting the knowledge of contextual information. Context-aware recommenders define the context as any information that characterizes the situations of items and users at a particular interaction. They are essential for some contexts where prediction can be more precise in generating specific personalized recommendations. This paper provides a comprehensive review of context-based recommendation systems in IoT environments, namely IoT-CARS, and sheds light on their requirements, characteristics, and applications. We characterize context-aware recommenders in terms of the different IoT contexts and how these contexts are modeled. We also highlight the used metrics to evaluate the performance of various context-based recommenders.

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Section: Discussionmentioning

confidence: 99%

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Nawara

Kashef

2021

IEEE Access

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“…Online community allows any member to involve in the construction of the community (Huang et al, 2020). Schau et al (2009) believed out that these collective social actions and interactions can create value.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Members' motivation to join a community is to meet their utilitarian or hedonic needs through social interaction with community members (Schubert & Ginsburg, 2000). Members communicate with each other on the community theme and publish user‐generated content to co‐create values (Huang et al, 2020). They also enjoy their time in the community.…”

Section: Introductionmentioning

confidence: 99%

Exploring the mediating role of utilitarian value and hedonic value in the formation of oppositional loyalty in online communities

Chen

Lang

et al. 2023

Int J Consumer Studies

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Our research expands the scope of the research on oppositional loyalty from brands to online communities. Online communities allow members to freely express their opinions and promote the occurrence of oppositional loyalty behaviours towards the communities. Oppositional loyalty is defined in this article that, for the purpose of strengthening the market position of their preferred community, members of the online community may express negative views or even show oppositional behaviour towards adversarial communities. In view of social capital theory, the study examines the effect of hedonic value and utilitarian value on oppositional loyalty in online communities. The results show that only hedonic value significantly affects oppositional loyalty; hedonic value mediates the effect of the three dimensions of social capital (network ties, identification, and common language) on oppositional loyalty. The study identifies that community type moderates the impact of social capital (identification and common language) on hedonic value but does not moderate the effect of social capital on utilitarian value. Finally, we find that members' oppositional loyalty towards their community can promote their community participation. This research provides recommendations for online community managers to manage and benefit from members' oppositional loyalty.

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“…Naturally, event extraction can be divided into two sub-tasks: event argument extraction and event type detection. It has a wide range of applications in the fields such as intelligent question answering, information retrieval [2], automatic summarization [3], recommendation [4], etc.…”

Section: Introductionmentioning

confidence: 99%

TDJEE: A Document-Level Joint Model for Financial Event Extraction

2021

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Extracting financial events from numerous financial announcements is very important for investors to make right decisions. However, it is still challenging that event arguments always scatter in multiple sentences in a financial announcement, while most existing event extraction models only work in sentence-level scenarios. To address this problem, this paper proposes a relation-aware Transformer-based Document-level Joint Event Extraction model (TDJEE), which encodes relations between words into the context and leverages modified Transformer to capture document-level information to fill event arguments. Meanwhile, the absence of labeled data in financial domain could lead models be unstable in extraction results, which is known as the cold start problem. Furthermore, a Fonduer-based knowledge base combined with the distant supervision method is proposed to simplify the event labeling and provide high quality labeled training corpus for model training and evaluating. Experimental results on real-world Chinese financial announcement show that, compared with other models, TDJEE achieves competitive results and can effectively extract event arguments across multiple sentences.

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A Survey of Context-Aware Recommendation Schemes in Event-Based Social Networks

Cited by 12 publications

References 93 publications

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Exploring the mediating role of utilitarian value and hedonic value in the formation of oppositional loyalty in online communities

TDJEE: A Document-Level Joint Model for Financial Event Extraction

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