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Predicting Customer Behavior Patterns and Applying Recommender System by Machine Learning Algorithms and Its Effect on Customer Satisfaction

Kazemnasab Haji, Ali | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56308 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Akhavan Niaki, Taghi
  7. Abstract:
  8. In this research, it has been tried to use deep learning methods and embedding vector, in addition to user-item data, from user side information such as age, gender, city, etc., and also for item information such as product name, product category, etc. can be used to better understand customer behavior patterns and provide a relatively rich recommender system. The proposed model in this research has two phases, the first phase tries to identify the user and item feature vector and form the user similarity matrix and the user-item correlation matrix. The outputs of phase one are used as inputs of phase two. In the second phase of the model, using these inputs, Top-N recommendation are generated for the target user. Finally, the accuracy and effectiveness of the proposed model compared to the algorithms of support vector machine, k-nearest neighbor and association rules have been calculated and analyzed using the dataset of DigiKala company. The results show that the proposed model obtained a score of 0.5234 for the average absolute error and 0.3186 for the index of the root mean square error, which shows that the model has improved compared to other used models and can overcome with the challenge of cold start and data sparsity
  9. Keywords:
  10. Recommender System ; Embedding Vectors ; Deep Learning ; Similarity Measure ; Cold Start ; Sparsity Constraint ; Customer Satisfaction Index Model ; Consumer Behavior

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