Personalized advertisement system using social relationship based user modeling

Ha, Inay; Oh, Kyeong-Jin; Jo, Geun-Sik

doi:10.1007/s11042-013-1691-6

Cited by 16 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many applications benefit from reliable approaches of user profiling in social networks. Such applications exist in various fields, from personalized advertising to reputation management (Dijkmans et al 2015;Ha et al 2015). In this section, we present and evaluate a PSL Q program for inferring age and gender of social network users.…”

Section: Node Labeling: User Profiling In Social Networkmentioning

confidence: 99%

Soft quantification in statistical relational learning

et al. 2017

View full text Add to dashboard Cite

We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as "most" and "a few." We define the syntax and the semantics of this language, which we call PSL Q , and present a most probable explanation inference algorithm for it. To the best of our knowledge, PSL Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for two real-world applications, link prediction in social trust networks and user profiling in social networks, demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves inference accuracy.

show abstract

Section: Node Labeling: User Profiling In Social Networkmentioning

confidence: 99%

Soft quantification in statistical relational learning

et al. 2017

View full text Add to dashboard Cite

show abstract

“…At present, there are more than 249 million microbloggers in China. Due to this considerable increase in the number of users and potential business applications, microblog can bring in business industry tremendous values such as advertising recommendation [3,4] and monitoring public opinions [5–7].…”

Section: Introductionmentioning

confidence: 99%

A three-layer model on users’ interests mining

Yang

Xie

Chen

2017

Journal of Information Science

View full text Add to dashboard Cite

With the advent of big data era, social media plays an important role in many areas such as security and finance. Researchers pay more attention on mining users’ interests through the social media data. A three-layer model (TLM) based on keyword extracting is proposed to mine users’ interests, which includes candidate words extracting, semantic structures analysing and interest words ranking. The TLM mainly focuses on both self-importance and semantic-importance of interest words. In addition, the TLM also considers the interest drifting to track long-term and short-term interests of users. Experiments conducted on 10 SINA Weibo datasets show that TLM is more efficient than existing methods to identify users’ interests based on hit rate.

show abstract

“…() presented IP models and several heuristics for the targeted advertising problem, which involves assigning ads to individual viewers who belong to the ad's target population with the goal of maximizing total revenue. Moreover, there are personalized advertisement recommendation systems based on user preference and social network information (Ha et al., ).…”

Section: Introductionmentioning

confidence: 99%

A multiobjective approach for maximizing the reach or GRP of different brands in TV advertising

Evangelista

Regis

2017

Int Trans Operational Res

View full text Add to dashboard Cite

This paper focuses on a multiobjective optimization problem in TV advertising from an advertising agency's perspective, which involves deciding on which commercial breaks to air the ads of various brands to jointly maximize reach or gross rating point (GRP) for the different brands subject to budget constraints, brand competition constraints, and other scheduling constraints. We present a multiobjective integer programming formulation of this problem and develop and implement algorithms for generating provably Pareto‐optimal solutions. We also develop reduction and visualization procedures to aid a decision maker in choosing suitable subsets of the Pareto‐optimal solutions obtained. Numerical experiments on five TV advertising problems involving 20–40 objective functions and thousands of decision variables and constraints demonstrate the effectiveness of the proposed formulation and solution methods in generating Pareto‐optimal objective vectors that reflect brand priorities and that are well distributed along the Pareto front.

show abstract

Personalized advertisement system using social relationship based user modeling

Cited by 16 publications

References 22 publications

Soft quantification in statistical relational learning

Soft quantification in statistical relational learning

A three-layer model on users’ interests mining

A multiobjective approach for maximizing the reach or GRP of different brands in TV advertising

Contact Info

Product

Resources

About