Personalized E-Advertisement and Experience: Recommending User Targeted Ads

Qudah, Dana Al; Cristea, Alexandra I.; Bazdarevic, Suncica Hadzidedic; Al-Saqqa, Samar; Rodan, Ali; Yang, Wei

doi:10.1109/icebe.2015.19

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…CTR intuitively measures user interest in specific products, allowing for the scoring of various products. AI recorded user's click histories and recommends similar advertisements based on this information [24]. To refine the function, indicator functions such as clicks or non-clicks, can be transformed into multi-score ratings.…”

Section: Click-through Ratementioning

confidence: 99%

Core Technologies in Recommender Systems: Investigating and Analyzing Standard Implementations

Cai

2024

HSET

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This paper places special emphasis on the evolution of recommendation algorithms in the context of big data and machine learning, underscoring significant advancements in deep learning and natural language processing that have markedly improved the precision and personalization of recommendations. We explore case studies in various sectors, including e-commerce, streaming services, and social media, to illustrate the adaptation of these technologies to distinct industry requirements. A critical component of our analysis is the examination of the impact of user data privacy regulations on the design and functionality of these systems. Additionally, the paper addresses the challenges and prospective directions in the field, with a particular focus on ethical considerations, the mitigation of bias, and the incorporation of artificial intelligence for dynamic, context-aware recommendation systems. Our research methodology amalgamates an extensive literature review with empirical data analysis, offering an in-depth understanding of the current state of recommendation system technologies. The findings of this study are intended to contribute to the field by presenting a comprehensive view of the technological, ethical, and practical dimensions of recommendation systems in the contemporary digital landscape.

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Section: Click-through Ratementioning

confidence: 99%

Core Technologies in Recommender Systems: Investigating and Analyzing Standard Implementations

Cai

2024

HSET

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“…Several papers have been written that analyze and survey recommender systems in the field of web personalization [5]. Furthermore, various approaches have been proposed in the context of e-commerce, such as statistical approaches [18] and machine learning frameworks. Algorithms such as Collaborative Filtering [24], Deep Learning Neural Networks [10] and SVM models [14] have been widely used in personalized content recommendation systems.…”

Section: Related Workmentioning

confidence: 99%

Banner Personalization for e-Commerce

Maniadis

Vavliakis

Symeonidis

2019

IFIP Advances in Information and Communication Technology

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Real-time website personalization is a concept that is being discussed for more than a decade, but has only recently been applied in practice, according to new marketing trends. These trends emphasize on delivering user-specific content based on behavior and preferences. In this context, banner recommendation in the form of personalized ads is an approach that has attracted a lot of attention. Nevertheless, banner recommendation in terms of e-commerce main page sliders and static banners is even today an underestimated problem, as traditionally only large e-commerce stores deal with it. In this paper we propose an integrated framework for banner personalization in e-commerce that can be applied in small-medium e-retailers. Our approach combines topic-models and a neural network, in order to recommend and optimally rank available banners of an e-commerce store to each user separately. We evaluated our framework against a dataset from an active e-commerce store and show that it outperforms other popular approaches.

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Large Scale Evaluation of an Adaptive E-Advertising User Model

Qaffas

Cristea

2016

E-Business and Telecommunications

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Personalized E-Advertisement and Experience: Recommending User Targeted Ads

Cited by 3 publications

References 14 publications

Core Technologies in Recommender Systems: Investigating and Analyzing Standard Implementations

Core Technologies in Recommender Systems: Investigating and Analyzing Standard Implementations

Banner Personalization for e-Commerce

Large Scale Evaluation of an Adaptive E-Advertising User Model

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