An approach to identify user preferences based on social network analysis

Milovanović, Stevan; Bogdanović, Zorica; Лабус, Александра; Barać, Dušan; Despotović-Zrakić, Marijana

doi:10.1016/j.future.2018.10.028

Cited by 20 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…& Control Syst. • • • • Miklosik, Kuchta, Evans, and Zak (2019) IEEE Access Computer Sciences • • • • Milovanović, Bogdanović, Labus, Barać, and Despotović-Zrakić (2019) Future Generation Computer Systems Computer Sciences • • • Naqvi, Awais, Saeed, and Ashraf (2018) Inter. J. of Advanced Computer Science and Applications Com.…”

Section: Methodology Developmentmentioning

confidence: 99%

Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics

Saura

2021

Journal of Innovation & Knowledge

311

131

View full text Add to dashboard Cite

In the last decade, the use of Data Sciences, which facilitate decision-making and extraction of actionable insights and knowledge from large datasets in the digital marketing environment, has remarkably increased. However, despite these advances, relevant evidence on the measures to improve the management of Data Sciences in digital marketing remains scarce. To bridge this gap in the literature, the present study aims to review (i) methods of analysis, (ii) uses, and (iii) performance metrics based on Data Sciences as used in digital marketing techniques and strategies. To this end, a comprehensive literature review of major scientific contributions made so far in this research area is undertaken. The results present a holistic overview of the main applications of Data Sciences to digital marketing and generate insights related to the creation of innovative Data Mining and knowledge discovery techniques. Important theoretical implications are discussed, and a list of topics is offered for further research in this field. The review concludes with formulating recommendations on the development of digital marketing strategies for businesses, marketers, and non-technical researchers and with an outline of directions of further research on innovative Data Mining and knowledge discovery applications.

show abstract

Section: Methodology Developmentmentioning

confidence: 99%

Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics

Saura

2021

Journal of Innovation & Knowledge

311

131

View full text Add to dashboard Cite

show abstract

“…The idea of a social network and its analysis has existed for more than 100 years, but it was not until the second half of the 20th century that scientists began to deal with it. With the advent of computers, SNA expanded into a discipline with its own approach to this paradigm, including clearly defined theory and analytical software [35,36,37].…”

Section: A Customer Data Collectionmentioning

confidence: 99%

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

Semerádová

Weinlich

2019

IEEE Access

View full text Add to dashboard Cite

The advertising systems and the algorithms they use are constantly evolving and expanding the possibilities for reaching potential customers. Hyper-targeting (also called microtargeting) is the use of detailed customer data and marketing automation to deliver highly targeted and personalized messages across a large number of channels. These campaigns are designed to appeal to specific people or small groups of customers. By using the ability to process large amounts of data through innovations, such as predictive analytics, marketers can gain a deeper understanding of their audiences, focusing on specific accounts and not on the entire segments. This reportedly allows B2B brands to target customers directly and provide unique personal and highly relevant experiences. However, the scientific evidence to support this claim is missing. Some previous studies even suggest a negative impact of highly personalized advertising content on user reactiveness and purchase behavior. In this article, we test the effects of different levels of personalized advertisements using the advanced campaign targeting tool called Facebook Lookalike Audiences. Facebook Lookalike Audiences works on the basis of the estimation of customer similarity based on the characteristics of a custom audience, as defined by the advertiser. We examine the performance of various targeting settings using data from 840 Facebook ads with different personalization levels. These advertisements are compared in terms of reach, number of reactions, frequency of impressions, number of clicks, average time spent on a website, number of viewed pages, number of conversions, and profitability. We believe that the findings presented in this article help clarify the factors that influence user reactiveness toward personalized online advertising using evidence from actual Facebook ad sets.

show abstract

“…A straightforward method to compute the similarities between users (or items) is to utilize the preferences of users (or attributes of items). However, like in the traditional social networks, [13][14][15][16][17] few users in EBSNs describe their detailed preferences for purpose of privacy. Fortunately, users in EBSNs have huge behaviors information which could reflect their preferences.…”

Section: Introductionmentioning

confidence: 99%

Social event planning using hybrid pairwise Markov random fields

Xiao

Saidutta

Fekri

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Event‐based social networks (EBSNs) have become increasingly popular, which provide online social event management platforms for event organizers to publish and share social events (e.g., outdoor activities). In EBSNs, a major challenge for a social event organizer is how to plan a social event to attract the maximum number of attendance. To organize an event, three essential elements are required, namely, what (i.e., event content), where (i.e., event location), and when (i.e., event time). In this paper, we focus on the social event planning problem, which selects a location and time to hold a social event for the organizer with the given event content, to maximize the total number of participants. The solution of the social event planning problem could support decision‐making for social event organizers. For simplicity, we denote a location and time pair as an item in this paper. To solve the social event planning problem, we present a hybrid pairwise Markov random field (H‐PMRF) model which takes latent preferences of users, latent attributes of items, similarities between users and similarities between items into consideration. In particular, we construct an undirected graph where each node represents a user's decision on a specific item and each edge represents the relationship between the nodes, define the node potentials and edge potentials which model the dependency relationships between nodes, and give a joint probability distribution over the graph. Further, we adopt the Loopy Belief Propagation algorithm to compute the posterior probability distribution of each node in H‐PMRF and select the location and time to hold the event which could attract the maximum number of participants. We collect real‐world data set from DoubanEvent website and conduct extensive experiments on it. Experimental results show that the proposed model outperforms several baselines.

show abstract

An approach to identify user preferences based on social network analysis

Cited by 20 publications

References 15 publications

Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics

Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

Social event planning using hybrid pairwise Markov random fields

Contact Info

Product

Resources

About