Affiliation:
1. Umea University, Sweden
Abstract
In this chapter, the authors describe an architecture for reinforcement learning in social media marketing. The rule bases used for action selection within the architecture build upon many-valued (fuzzy) logic. Action evaluation and internal learning is based on neural network like structures. In using variables measuring the effect of advertising, we must understand direction of influence between advertiser, owning the content of the advertisement, and advertisee, as the target of an advertisement, and as facilitated by social media marketing. Examples are drawn from Facebook marketing.
Reference18 articles.
1. The Development of the Hierarchy of Effects: An Historical Perspective.;T. E.Barry;Current Issues and Research in Advertising,2012
2. Learning and tuning fuzzy logic controllers through reinforcements
3. Bezdek, J. C. J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press.
4. Social media sentiment analysis: lexicon versus machine learning
5. Network size versus preprocessing;P.Eklund;Fuzzy Sets, Neural Networks and Soft Computing,1994