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Analysis of Movie Rating Prediction Using Multilayer Perceptron and SMO Techniques

This project presents a comparative analysis between Multilayer Perceptron (MLP) and Support Vector Machines (SMO) for predicting movie ratings using a dataset spanning 1980 to 2005. Utilizing Weka 3.4 on a Windows XP system with 1GB RAM, we analyze data attributes including MovieID, CustomerID, and Date. The study reveals that performance issues stem from a limited dataset size, weak inter-attribute relationships, and class imbalance in ratings around 3 and 4. This work emphasizes the importance of dataset quality in machine learning outcomes.

jamal-weeks
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Analysis of Movie Rating Prediction Using Multilayer Perceptron and SMO Techniques

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  1. Final ProjectGroup Number:19 M9615011林明德 M9615077黃瀚平

  2. Method and System • System: Windows XP ,1GBRam • Tool: Weka 3.4 • Function: MultilayerPerceptron & SMO • Attribute:MovieID, CustomerID, Date(year) • Data: 年份(1980~2005)、MovieID(1~1000) Movie中data筆數:300 Total data num.196019

  3. Result(Mulitlayer VS. SMO) MultilayerPerceptron SMO

  4. Result(tuning about learn rate) Rate:0.1 Rate:0.4

  5. Result(test data)

  6. Why bad performance? • 1.選取的 Data set 不夠大 • 2.Attribute間的關聯性不夠明顯且Class分佈偏重於rating 3,4

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