Yazar "Akarsu, Ozgur" seçeneğine göre listele

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    Building A Non-Personalized Recommender System by Learning Product and Basket Representation
    (IEEE, 2020) Yildirim, Savas; Soyler, Sebnem Gunes; Akarsu, Ozgur
    In this paper, we addressed the problem of learning product and basket representation for a non-personalized recommendation system where the baskets do not have a specific owner. The recommendation models tend to exploit as much information as possible along with basket patterns to improve performance. We focus on the representation problem for the baskets without any customer information. Deep learning-based architectures have solved many representation problems such as natural language processing (NLP) and computer vision (CV) so far. While the NLP model takes a bag of words as input, the recommendation models take a basket of products as input. The learning algorithm uses co-occurrence information and therefore exploits the idea that the things that appear in a similar environment share similar meaning. But traditional representation approaches such as one-hot encoding have dimensionality problems when the number of entities increases. On the other hand, neural models can solve this dimensionality curse and transform each entity into a short and dense vector, namely embeddings. We successfully designed unsupervised and supervised architectures to solve the product and basket embeddings for a recommendation engine. Our experiments show that the proposed deep learning architecture showed better performance than baseline approaches in terms of many metrics. We also discussed and addressed many product representation related problems throughout the paper.
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    Listening to the organization: change evaluation with discourse analysis
    (Emerald Group Publishing Ltd, 2018) Akarsu, Ozgur; Gencer, Mehmet; Yildirim, Savas
    Purpose Change is continuous and leaves many digital traces in contemporary organizations, while research on change usually lacks such continuity. The purpose of this paper is to test and explore the claim that change can be monitored through employee discourse. In doing so, the authors introduce basic text mining methods to detect prevailing keywords and their changes over time. Such monitoring of content and its change promises a continuous feedback and improvement for change management efforts. Design/methodology/approach The authors use a mixed research design, combining an ethnographic approach with digital methods. The quantitative element of the method involves applying text mining techniques to a document corpus that is representative of people in organizations, and is originally collected as part of a relatively common performance management system. The findings about discursive categories and their change patterns through time are then combined with observations and secondary information about change management for interpretation. Findings By combining these measurements with additional information about the change program in focus, the authors develop an interpretation of the dynamics of organizational change. Results showed that even in a successfully implied change effort that realize the planned targets, change does not occur directly and fully, with some elements of discourse being more persistent than others. Research limitations/implications Method of the research presents a new way of monitoring discursive change. Its incorporation into practice potentially allows for timely correction of change efforts and increasing possibility of success. Originality/value This research provides a framework for understanding how, and to what extent, planned change efforts effect organizations. Furthermore, the method developed in this research presents an innovative approach to monitor discursive change and timely managerial intervention.