Özlük, Özgür
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Özgür Özlük
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Email Address
ozluko@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
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Scholarly Output
24
Articles
3
Views / Downloads
5052/41834
Supervised MSc Theses
20
Supervised PhD Theses
0
WoS Citation Count
17
Scopus Citation Count
19
WoS h-index
2
Scopus h-index
3
Patents
0
Projects
1
WoS Citations per Publication
0.71
Scopus Citations per Publication
0.79
Open Access Source
21
Supervised Theses
20
Google Analytics Visitor Traffic
| Journal | Count |
|---|---|
| Annals Of Operations Research | 1 |
| Journal of Public Procurement | 1 |
| Journal of the Operational Research Society | 1 |
| Proceedings of International Conference on Computers and Industrial Engineering, CIE | 1 |
Current Page: 1 / 1
Scopus Quartile Distribution
Competency Cloud
Scholarly Output Search Results
Now showing 1 - 10 of 24
Master Thesis Predicting Facebook Ad Impressions & Cpm Values(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tekten, Semih; Özlük, ÖzgürIt is estimated that there are more than two billion active users on Facebook as of the first quarter of 2018 and social media has tremendous opportunities for advertisers in terms of performance and measurability. However, for marketing managers, it is very difficult to manage all the campaigns on different marketing channels and optimize for better results.For that reason, Facebook Marketing Partners or other optimization solutions emerged in the adtech market. In order to improve existing optimization solutions in the market, ad impression costs will be predicted in this study by using different machine learning techniques and different algorithms. The main goal of this study is to generate a robust model for predicting CPM values on Facebook, and to use that model as an in put for the existing optimization solution Adphorus offers for its clients. Adphorus is one of the Facebook Marketing Partners in the market.Master Thesis Sentiment Analysis of Hürriyet Emlak(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Korkmaz, Alev; Özlük, ÖzgürSentiment analysis refer to the task of natural language processing to determine whether a piece of text contains some subjective information and what subjective information it expresses, whether the attitude behind a text is positive, negative or neutral.Master Thesis Airbnb Host Recommendation Engine(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Arslan, Batuhan; Özgür ÖzlükIn this project, a fifth rule is proposed to reveal guests ' comments about hosts using the recommendation system and sentiment analysis for the super hosts' selection for Airbnb. This project is aimed to contribute to Airbnb's selection of Super hosts. In this study, sentiment analysis and comment data are examined, and polarity scores are created for use in suggestion systems. A collaborative filtering method is used for the recommendation system. The FunkSVD algorithm received the best RMSE score. Polarity scores are estimated for each latent user by looking at the host and listing id. The recommendation system developed ranked the polarity scores of hosts for each user.Master Thesis Text Classification Using Apache Spark(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Azizoğlu, Umut Rezan; Özlük, ÖzgürOne of the biggest problems of enterprises which are marketplace e-commerce business model with social platform; The improper communication of their social platform is the negative impact of the customer experience and the damage of the brand's value both materially and morally. As the number of daily commentaries is in numbers that cannot be read manually with optimal human resources in terms of company profitability, the interpretation modules in social market places are left unconscious. With this Project; established a model that prevents sentences that spoil the customer experience in their social platforms. Both data preparation and machine learning model were developed on Databricks notebook, using the apache spark platform with SparkML libraries and Pyspark language. The “Text Classification” approach is adopted when determining the model.Conference Object Capacity Allocation and Pricing Policies for Cloud Computing Service Providers(Curran Associates Inc., 2018) Ünlüyurt T.; Özgür Özlük; Afghah, R.The cloud computing is regarded as a paradigm shift in today’s IT world. As cloud computing resources behave like perishable products, revenue management techniques can be applied to increase cloud service provider's total revenue. In this paper, we propose various methods for pricing and capacity allocation. We consider three types of instances offered by the service provider; subscription, on-demand and spot instances. We introduce three allocation and pricing policies and propose different models. We simulate these models on a randomly generated dataset and evaluate the models for different capacities. The results we obtain indicate the sensitivity of revenue to varying policies and demonstrate the potential profit increase for cloud service providers. © 2018, Curran Associates Inc. All rights reserved.Master Thesis Duplicate Record Detection: a Rule-Based Approach(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Malkaralı, Gülce; Özgür ÖzlükThe study presents a rule based algorithm to detect dublicate and near-dublicate rocords within a dataset that is extracted from a leading online reality platform.Master Thesis Forecasting Organic Traffic With Different Source of Data(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çolak, Mehtap; Özgür ÖzlükIn this project, the results are compared using different data sets for the organic traffic forecasting of a website. Two different models were developed based on the data obtained from Google Search Console (GSC), Google Analytics (GA), Ahrefs and Google Trends and trained with XGBoost and Random Forest machine learning algorithms. Although the .. value and accuracy rate of the first model developed on the GSC, GA and Ahrefs data obtained between 2019-2020 was high; it is not suitable for predictive analysis because the data sets consist of dependent variables. The second model was developed with Google Trends data for brand and non-brand queries with the highest Impression value. The future trends of the relevant queries were predicted using the Prophet algorithm. Through this model, Impression values of the relevant website were estimated for the remainder of 2021.Master Thesis The Effect of Bert-Based Grammatical Analysis on Google Search Results(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çolak, Oğuz; Özgür ÖzlükThis study aims to study the BERT, namely Bidirectional Encoder Representations from Transformers model, which is introduced by Google and is of great importance in content analysis, and to examine the role of grammatical accuracy in the process of content quality measurement and Search Engine Results Pages (SERP). BERT has an important role among the algorithms used by Google in order to maintain the quality of search results and to provide more relevant content to users by understanding the content more effectively.In this study, CoLA data, which is accepted as the most reliable data in this field and therefore used frequently in similar BERT studies, is used. The main purpose here is to make a BERT-based grammatical evaluation of sentences in a content and then examine these results on pages with optimal ranking values, to examine the connection between search results and grammatical accuracy and the importance of this parameter.In this context, the project consists of two phases. In the first phase, the content of the pages that are visible in the first 20 in 50 different queries are scored with the pre-trained BERT model. In the second phase, a dataset that includes different SEO-focused metrics of the same pages is created manually, and the importance of the BERT score among these features is investigated.Master Thesis Tractor Sales Forecast Using Machine Learning(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunay, Yiğitcan; Özlük, ÖzgürThis study presents a machine learning model to forecast tractor sales using four years of number of tractor sales based on year, month, city, town, brand and model provided by Turkey Statistical Institute. Tractor sales can vary depending on many different factors. Therefore, it is a challenging task for any company to estimate number of tractor sales that will be sold next year. Having the ability to predict that accurately will contribute companies in many distinct ways. Foreseeing market trends, keeping pace with the competition, delivering the right product to the right customer at the right time, reducing inventory costs, better production planning and cash flow management are major advantages of accurate forecasting. Within the scope of this study, models were developed to predict tractor sales using different statistical and machine learning methods. In further steps of the study, meaningful variables can be added to the dataset in order to reach a better result. Also, market share can be estimated by using different simulation methods which take into consideration those variables.Article Citation - WoS: 6Citation - Scopus: 5Optimal Keyword Bidding in Search-Based Advertising With Budget Constraint and Stochastic Ad Position(Taylor & Francis, 2019) Özlük, Özgür; Selçuk, Barış; Küçükaydın, HandeThis paper analyses the search-based advertising problem from an advertiser’s view point, and proposes optimal bid prices for a set of keywords targeted for the advertising campaign. The advertiser aims to maximise its expected potential revenue given a total budget constraint from a search-based advertising campaign. Optimal bid prices are formulated by considering various characteristics of the keywords such that the expected revenue from a keyword is a function of the ad’s position on the search page, and the ad position is a stochastic function of both the bid price and the competitive landscape for that keyword. We explore this problem analytically and numerically in an effort to generate important managerial insights for campaign setters.
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