Evolving context-aware recommender systems with users in mind

Livne, Amit; Tov, Eliad Shem; Solomon, Adir; Elyasaf, Achiya; Shapira, Bracha; Rokach, Lior

doi:10.1016/j.eswa.2021.116042

Cited by 17 publications

(9 citation statements)

References 25 publications

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“…RSs can be categorized into three groups: content-based (CB), CFbased, and hybrid. CB systems seek to match content with user interests (Livne et al, 2022). CF is the most popular, widely-used technique in the recommendation environment.…”

Section: Cf and Matrix Factorizationmentioning

confidence: 99%

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Khaledian

Nazari

Khamforoosh

et al. 2023

Expert Systems with Applications

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Section: Cf and Matrix Factorizationmentioning

confidence: 99%

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Khaledian

Nazari

Khamforoosh

et al. 2023

Expert Systems with Applications

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“…With the increasing development of mobile internet, context-aware recommendation has become one of the most active research topics in recommender system [1,2]. Some researchers present a recommender system based on the prediction of traffic conditions in the internet of vehicles environment [3].…”

Section: Related Workmentioning

confidence: 99%

Collaborative recommendation for user spatial activities in mobile environments

Zhang,

Zhang

2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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Recommendation technologies are facing new challenges in mobile environments due to the complexity of user behaviors in dynamic contexts. In this paper, we focus on the integration of internet content access with user natural behaviors, and propose a context-aware collaborative recommendation paradigm for user spatial activities in mobile environments. In the proposed approach, potential user behavior patterns with contexts and preferences are discovered from historical logs. Then, temporal activity prediction and service recommendation tasks are performed according to the target user's realtime behavioral contexts using an improved collaborative filtering algorithm. Analysis and experiments indicate that our approach can effectively improve the quality of service recommendation in mobile computing environments.

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“…High-level context, which is more meaningful and suitable for reasoning, can be extracted from low-level data through advanced processing techniques (Perera et al, 2014). Machine learning techniques were adopted to discover hidden contexts from raw data (Livne et al, 2022;Unger et al, 2016). For example, Campana and Delmastro (2021) apply clustering algorithms to extract environmental context (e.g., location, nearby devices) from the mobiles.…”

Section: Behavioral Aspectmentioning

confidence: 99%

Introduction to the Minitrack on Human-Computer Interaction in the Digital Economy

Schneider,

Valacich,

Jenkins

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Context awareness systems, a subcategory of intelligent systems, are concerned with suggesting relevant products/services to users' situations as smart services. One key element for improving smart services' quality is to organize and manipulate contextual data in an appropriate manner to facilitate knowledge generation from these data. In this light, a knowledge-based approach, can be used as a key component in context-aware systems. Context awareness and knowledge-based systems, in fact, have been gaining prominence in their respective domains for decades. However, few studies have focused on how to reconcile the two fields to maximize the benefits of each field. For this reason, the objective of this paper is to present a literature review of how contextaware systems, with a focus on the knowledge-based approach, have recently been conceptualized to promote further research in this area. In the end, the implications and current challenges of the study will be discussed.

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Evolving context-aware recommender systems with users in mind

Cited by 17 publications

References 25 publications

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Collaborative recommendation for user spatial activities in mobile environments

Introduction to the Minitrack on Human-Computer Interaction in the Digital Economy

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