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CSE40028 Introduction to Programming
Questions:
Purchase prediction Predict given a (user,item) pair from ‘pairs Purchase.txt’ whether the user purchased the item (really, whether it was one of the products they reviewed). Accuracy will be measured in terms of the categorization accuracy (fraction of correct predictions). The test set has been constructed such that exactly 50% of the pairs correspond to purchased items and the other 50% do not. Category prediction Predict the category from a review. Five categories are used for this task, which can be seen in the baseline program, namely Women’s, Men’s, Girls’, Boys’, and Baby clothing. Performance will be measured in terms of the fraction of correct classifications. Rating prediction Predict people’s star ratings as accurately as possible, for those (user,item) pairs in ‘pairs Rating.txt’. Accuracy will be measured in terms of the mean-squared error (MSE). Evaluation
• Your ability to obtain a solution which outperforms the baselines on the unseen portion of the test data. A solution which is substantially better (i.e., at least 90% percent and above) and better than baseline performance.
• Your best possible solution for each of the tasks must be far better than the baseline.
• Obtain a solution which outperforms the baselines on the seen portion of the test data
• Finally, your written report should describe the approaches you took to each of the tasks. To obtain good performance, you should not need to invent new approaches (though you are more than welcome to!) but rather you will be graded based on your decision to apply reasonable approaches to each of the given tasks
Introduction
In this assignment you will build recommender systems to make predictions related to reviews of Clothing, Shoes, and Jewellery on Amazon. Solutions will be graded by the performance of your system relative to naive baselines with additional points for the winning systems in each category
Baselines
Simple baselines have been provided for each of the tasks. These are included in ‘baselines.py’ among the files above. These baselines operate as follows:
Purchase prediction Find the most popular items that account for 50% of purchases in the training data. Return ‘1’ whenever such an item is seen at test time, ‘0’ otherwise.
Category prediction Look for a few likely words that may appear in reviews of each category (e.g. if the word ‘baby’ appears, classify as Baby clothing).
Rating prediction Return the global average rating, or the user’s average if we have seen them before in the training data.
Running ‘baselines.py’ produces files containing predicted outputs. Your submission files should have the same format.
CSE40028 Introduction to Programming
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