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    An Additive FAHP Based Sentence Score Function for Text Summarization
    (Kaunas Univ Technology, 2017) Guran, Aysun; Uysal, Mitat; Ekinci, Yeliz; Guran, Celal Barkan
    This study proposes a novel additive Fuzzy Analytical Hierarchy Process (FAHP) based sentence score function for Automatic Text Summarization (ATS), which is a method to handle growing amounts of textual data. ATS aims to reduce the size of a text while covering the important points in the text. For this aim, this study uses some sentence features, combines these features by an additive score function using some specific weights and produces a sentence score function. The weights of the features are determined by FAHP - specifically Fuzzy Extend Analysis (FEA), which allows the human involvement in the process, uses pair-wise comparisons, addresses uncertainty and allows a hierarchy composed of main features and sub-features. The sentences are ranked according to their score function values and the highest scored sentences are extracted to create summary documents. Performance evaluation is based on the sentence coverage among the summaries generated by human and the proposed method. In order to see the performance of the proposed system, two different Turkish datasets are used and as a performance measure, the F-measure is used. The proposed method is compared with a heuristic algorithm, namely Genetic Algorithm (GA). Resulting performance improvements show that the proposed model will be useful for both researchers and practitioners working in this research area.
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    Development of a hybrid model to plan segment based optimal promotion strategy
    (Sage Publications Ltd, 2023) Ekinci, Yeliz; Guran, Aysun
    The study addresses the long-term effects of promotions in terms of movement in a value-based segmentation (lead, iron, gold, platinum), instead of simply looking at response rates that occur shortly after the promotion. The study develops a framework for planning an optimal promotion strategy via Markov Decision Processes and Machine Learning methods for an online department store. In the first phase, the states are set as the customer profitability segments in order to conduct the MDPs. Then, MDP model is solved, and the optimal decision for each segment is determined. In the second phase, in order to aid the company for making their plans for the next year, the segment that the customer will belong to next year should be predicted. Prediction of the future customer profitability segment is performed by using several machine learning algorithms, and the best performing model is selected. Using this best performing model, the company can predict the future (potential) profitability segment of the customer and make plans which include the optimal promotions that will be directed to the customers depending on their segments (these optimal promotions are the outcomes of the first phase). The proposed framework can be applied by practitioners in e-commerce companies which keep customer data.