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CMPT 291 Section OP01 Database Programming
创建日期：2024-06-26 17:13:17
标签： CMPT 291
Section OP01 Database Programming

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CMPT 291 Section OP01 Database Programming


Objectives

The goal for this lab is to compute the mean absolute error of an experiment using the “leave one out” strategy explained in the lecture on evaluating recommender systems.

Demonstrating/Submitting

To get credit for the lab you must either demonstrate the lab to a TA/instructor during office hours or record a demo, host it online, and include a link along with your submitted code on Brightspace. You can use the Kaltura Capture and MediaSpace resources provided by Carleton or your own software/hosting resources to record and share the demonstration. For partners, only one demonstration or submission is required. If demonstrating during office hours, inform the instructor/TA of your partner’s name. If submitting on Brightspace, include both partners’ names in a README file.

Lab Description

For this lab, you will be provided with a larger dataset (parsed-data-trimmed.txt) that follows a similar structure to the previously used ratings data. The full dataset will be used for the assignment. The only change to the structure of this data from the past labs is that the value 0 is now used to indicate no rating instead of -1.

The goal for this lab is to generate an output of the mean absolute error achieved by either the user-based or item-based recommendation algorithms that have been covered in the last few weeks. Whichever algorithm you decide to use, you should use a neighbourhood size of 5 for your calculation. You should also not include any similarities that are less than 0.

Now that we are working with data that is closer to what would be seen in a real world application, we will have to deal with some unexpected outcomes in our calculations. For example, if the numerator in any of your calculations comes out to 0, you will get an output of NaN/-Infinity/Infinity. These should not be included as real predictions. Instead, a suitable “best guess” to make in any of these cases would be the average rating score of the user (remember to compute it by ignoring the current rating that we are predicting). You may also run into problems where you want to use a neighbourhood of size X but there are not X suitable neighbours to use. In these cases, you should use as many neighbours as possible (up to a maximum of X).

For your demonstration, you should discuss how you have implemented the ‘leave one out’ strategy, how you have computed your predictions, and how you have computed the mean absolute error over all of your predicted ratings. It should be possible to achieve a MAE of ~0.77 using 5 neighbours and either the user-based or item-based recommendation algorithm.


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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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