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CSE 158/258 Assignment
创建日期：2024-07-29 15:16:14
标签： CSE 158
Assignment
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CSE 158/258 Assignment 
Instructions
In this assignment you will build recommender systems to make predictions related to book reviews from
Goodreads.
Submissions will take the form of prediction files uploaded to gradescope, where their test set performance
will be evaluated on a leaderboard. Most of your grade will be determined by ‘absolute’ cutoffs;
the leaderboard ranking will only determine enough of your assignment grade to make the
assignment FUN.
The assignment is due Monday, Nov 14, though make sure you upload solutions to the leaderboard
regularly.
You should submit two files:
writeup.txt a brief, plain-text description of your solutions to each task; please prepare this adequately in
advance of the submission deadline; this is only intended to help us follow your code and does not need
to be detailed.
assignment1.py A python file containing working code for your solutions. The autograder will not execute
your code; this file is required so that we can assign partial grades in the event of incorrect solutions, check
for plagiarism, etc. Your solution should clearly document which sections correspond to each
question and answer. We may occasionally run code to confirm that your outputs match submitted
answers, so please ensure that your code generates the submitted answers.1
Along with two files corresponding to your predictions:2
predictions Read.csv,predictions Category.csv,predictions Rating.csv Files containing your predictions for each (test) instance (you should submit two of the above three files). The provided baseline
code demonstrates how to generate valid output files.
This assignment should be completed individually. To begin, download the files for this assignment from:
http://cseweb.ucsd.edu/classes/fa22/cse258-a/files/assignment1.tar.gz
Files
train Interactions.csv.gz 200,000 ratings to be used for training. This data should be used for the ‘read
prediction’ (both classes) and ‘rating prediction’ (CSE258 only) tasks. It is not necessary to use all
ratings for training, for example if doing so proves too computationally intensive.
userID The ID of the user. This is a hashed user identifier from Goodreads.
bookID The ID of the book. This is a hashed book identifier from Goodreads.
rating The star rating of the user’s review.
train Category.json.gz Training data for the category prediction task (CSE158 only). This file is json
formatted, and contains the following fields:
n votes The number of ‘likes’ this review has received.
review id A hashed identifier for this review.
user id A hashed identifier for the user.
review text Text of the review.
rating Rating of the book.
genreID A numeric label associated with the genre.
genre A string version of the genre.
test Category.json.gz Test data associated with the category prediction task. This data has the same format
as above, with the ‘genre’ and ‘genreID’ labels hidden.
1Don’t worry too much about dependencies if importing non-standard libraries.
2You are welcome to submit all three, but will only be graded on those relevant to your section.
1
pairs Read.csv Pairs on which you are to predict whether a book was read (both classes).
pairs Category.csv Pairs (userID and reviewID) on which you are to predict the category of a book (CSE158
only).
pairs Rating.csv Pairs (userIDs and bookIDs) on which you are to predict ratings (CSE258 only).
baselines.py A simple baseline for each task, described below.
Please do not try to collect these reviews from Goodreads, or to reverse-engineer the hashing function I
used to anonymize the data. Doing so will not be easier than successfully completing the assignment. We
will execute code for any solution suspected of violating the competition rules to confirm that
it generates valid output; all code will be run through a plagiarism detector.
Tasks
You are expected to complete the following tasks:
Read prediction (both classes) Predict given a (user,book) pair from ‘pairs Read.csv’ whether the user
would read the book (0 or 1). 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 read books and the other 50% do not.
Category prediction (CSE158 only) Predict the category of a book from a review. Five categories are used
for this task, which can be seen in the baseline program, namely Children’s, Comics/Graphic Novels,
Fantasy, Mystery/Thriller, and Romance. Performance will be measured in terms of the fraction of
correct classifications.
Rating prediction (CSE258 only) 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).
A competition page has been set up on gradescope to keep track of your results compared to those of other
members of the class. The leaderboard will show your results on half of the test data, but your ultimate score
will depend on your predictions across the whole dataset.
Grading and Evaluation
This assignment is worth 25% of your grade. You will be graded on the following aspects. Each of the two
tasks is worth 10 marks (i.e., 10% of your grade), plus 5 marks for the written report.
• Your ability to obtain a solution which outperforms the leaderboard baselines on the unseen portion of
the test data (5 marks for each task). Obtaining full marks requires a solution which is substantially
better than baseline performance.
• Your ranking for each of the tasks compared to other students in the class (3 marks for each task).
• Obtain a solution which outperforms the baselines on the seen portion of the test data (i.e., the leaderboard). This is a consolation prize in case you overfit to the leaderboard. (2 mark for each task).
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 (5
marks total).
Baselines
Simple baselines have been provided for each of the tasks. These are included in ‘baselines.py’ among the files
above. They are mostly intended to demonstrate how the data is processed and prepared for submission to
gradescope. These baselines operate as follows:
Read prediction Find the most popular books that account for 50% of interactions in the training data.
Return ‘1’ whenever such a book is seen at test time, ‘0’ otherwise.
2
Category prediction Look for a few likely words that may appear in reviews of each category (e.g. if the
word ‘fantasy’ appears, classify as Fantasy).
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 (these outputs can be uploaded to gradescope). Your submission files should have the same format.
You are welcome to attempt the tasks from either class, but will only be graded on the tasks from your
own class.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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