Personalization in Practice: Methods and Applications

Goldenberg, Dmitri; Kofman, Kostia; Albert, Javier; Mizrachi, Sarai; Horowitz, Adam Haar; Teinemaa, Irene

doi:10.1145/3437963.3441657

Cited by 29 publications

(17 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It improves the user experience and enhances user loyalty by offering value to them. As personalization becomes more of an expected norm across all types of online platforms and services each year [46], numerous companies are taking advantage of interactive technology to personalize their interactions with users [47].…”

Section: Rumi Personalization and Machine Learningmentioning

confidence: 99%

“…The integration of personalization in various products turned rapidly from an unnecessary luxury to a commodity that is expected by customers [46]. The expectation made all companies adopt personalization so they would not fall behind the curve.…”

Section: Rumi Personalization and Machine Learningmentioning

confidence: 99%

“…This is where machine learning and AI come into the picture. Personalization is one of the key applications in machine learning with widespread usage in various industries [46]. Machine Learning offers a set of different techniques allowing one-to-one personalization.…”

Section: Rumi Personalization and Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

Jafari¹,

Zhao²,

Jafari³

2022

JSEA

View full text Add to dashboard Cite

The dramatic improvement of information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. This paper discusses how Artificial Intelligence and Machine Learning techniques are adopted to fulfill users' needs in a social learning management system named "CourseNetworking". The paper explains how machine learning contributed to developing an intelligent agent called "Rumi" as a personal assistant in CourseNetworking platform to add personalization, gamification, and more dynamics to the system. This paper aims to introduce machine learning to traditional learning platforms and guide the developers working in LMS field to benefit from advanced technologies in learning platforms by offering customized services.

show abstract

Section: Rumi Personalization and Machine Learningmentioning

confidence: 99%

Section: Rumi Personalization and Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

Jafari¹,

Zhao²,

Jafari³

2022

JSEA

View full text Add to dashboard Cite

show abstract

“…To accomplish this primary goal, BP modeling, which is a graphical presentation of processes in an organization, is employed [63]. Traditionally, recommender systems have relied on approaches such as clustering [64], closest neighbor [65], and matrix factorization [66] to overcome the aforementioned constraint. However, in recent years, we have seen remarkable success with what we term ML and DL for recommendation systems that are categorized into three types based on how the suggestions are generated [67]: Recommender Systems Based on Content, Recommender Systems Based on Collaborative Filtering and Recommender Systems in Hybrid Form.…”

Section: Ai Powers Business Processes Miningmentioning

confidence: 99%

A Road-map for Mining Business Process Models via Artificial Intelligence Techniques

Chaima¹,

KHEBIZI²

2022

International Journal of Informatics and Applied Mathematics

View full text Add to dashboard Cite

Nowadays, the size of data recorded and stored in enterprises information systems (IS) is increasing every second. To face to this phenomenon, contemporary technologies play a major role for gathering, analyzing, storing, and distributing data that enables organizations to make smart decisions and to take full control of their activities. The traditional Business Process (BP) mining techniques were intensively used to discover, monitor, and optimize processes from event-logs without needing any priory model. However, the majority of the suggested algorithms have exhibited their limits (such as discovering nested loops, managing duplicate and hidden tasks as well as dealing with concurrent processes). In parallel, recent advances in the Artificial Intelligence (AI) discipline have generated a great deal of enthusiasm in a large spectrum of research area. In this perspective, AI methods emerge as one of the pillars to overcome the drawbacks of the conventional approaches allowing anomalies detection, prediction and recommendation tasks on ongoing process instances at run-time. The aim of this work is to explore towards the use of AI techniques in the field of business process mining by presenting a state-of-the-art review ranging from traditional PM approaches to AI ones, as well as outlining a prospective road-map for mining business process models basing on AI techniques.

show abstract

“…Over the recent years, uplift modeling has become popular in web and e-commerce applications, such as at Facebook, Amazon [19] Criteo [4], Uber [34] and Booking.com [29]. Such models can be used for personalization purposes [8] since we can use the estimations to decide if a customer should be treated or not. They are also used to target a specific segment of the customer base with a promotional offer or other types of marketing campaigns.…”

Section: Introductionmentioning

confidence: 99%

E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling

Albert¹,

Goldenberg²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Promotions and discounts are essential components of modern e-commerce platforms, where they are often used to incentivize customers towards purchase completion. Promotions also affect revenue and may incur a monetary loss that is often limited by a dedicated promotional budget. We study the Online Constrained Multiple-Choice Promotions Personalization Problem, where the optimization goal is to select for each customer which promotion to present in order to maximize purchase completions, while also complying with global budget limitations. Our work formalizes the problem as an Online Multiple Choice Knapsack Problem and extends the existent literature by addressing cases with negative weights and values. We provide a real-time adaptive method that guarantees budget constraints compliance and achieves above 99.7% of the optimal promotional impact on various datasets. Our method is evaluated on a large-scale experimental study at Booking.comone of the leading online travel platforms in the world.

show abstract

Personalization in Practice: Methods and Applications

Cited by 29 publications

References 33 publications

Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

A Road-map for Mining Business Process Models via Artificial Intelligence Techniques

E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling

Contact Info

Product

Resources

About