Context-Aware Based Evolutionary Collaborative Filtering Algorithm

Gasmi, Ibtissem; Anguel, Fouzia; Seridi-Bouchelaghem, Hassina; Azizi, Nabiha

doi:10.1007/978-3-030-58861-8_16

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…This approach assumes that the context is defined with a predefined set of observable attributes, for which the structure does not change significantly over time [41]. Context information such as time, location, geometric information, or the accompaniment of other people (e.g., friends, girlfriend/boyfriend, relatives, or colleagues) has recently been considered in recommender systems [39,45,46].…”

Section: Context Aware-based Filteringmentioning

confidence: 99%

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Chicaiza

Valdiviezo-Díaz

2021

Information

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In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.

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Section: Context Aware-based Filteringmentioning

confidence: 99%

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Chicaiza

Valdiviezo-Díaz

2021

Information

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“…Recently, Alhijawi and Kilani in 2020 [20] proposed a novel GA-based collaborative filtering that aims to select the best items which meets the active user's preferences based on multi-filtering criteria. Another recent study by Gasmi et al in 2021 [21] also proposed a user-based collaborative filtering combined with the GA based meta-heuristic. A study by Xiao [22] proposed a combination of item-based collaborative filtering with GA which is called itemCFGA.…”

Section: Related Workmentioning

confidence: 99%

A Genetic-Based Pairwise trip Planner Recommender System

Qomariyah

Kazakov

2021

Preprint

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The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

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“…By leveraging vast datasets and sophisticated algorithms, AI can provide insights into battery health, predict degradation, and suggest real-time optimization strategies [5][6]. This paper delves into the AI-driven approaches that have been developed to enhance EV battery performance [7].…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Optimization of Battery Management for Enhanced EV Efficiency

2023

Proceeding Book of 2nd International Conference on Contemporary Academic Research ICCAR 2023

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In the rapidly evolving landscape of electric vehicles (EVs), optimizing battery performance remains a pivotal challenge. This study presents a comprehensive comparison between traditional battery management techniques and an AI-driven approach leveraging Convolutional Neural Networks (CNNs) for EVs. Our investigations focused on three primary metrics: prediction accuracy concerning the State of Charge (SoC) and battery health, potential battery life extension, and computational efficiency. Results unequivocally showed that the CNN-based model surpassed traditional methodologies in all examined metrics, with improved prediction accuracies, a significant increase in estimated battery lifespan, and reduced computational times. This research underscores the potential of integrating AI into EV battery management systems, promising not only enhanced battery performance but also signaling a paradigm shift towards a more sustainable, efficient, and reliable electric transportation era.

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Context-Aware Based Evolutionary Collaborative Filtering Algorithm

Cited by 7 publications

References 17 publications

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

A Genetic-Based Pairwise trip Planner Recommender System

AI-Driven Optimization of Battery Management for Enhanced EV Efficiency

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