Python代写 | FIT5212 Assignment 2


FIT5212 Assignment 2

Task 1: Recommender System Challenge (70% Marks)
This dataset is collected from an online social network platform. People interact with others by making
friends and posting reviews and ratings for different items. Please recommend a list of items to each
The user item interaction data is the main data for this challenge. This data is further split into training,
validation, and test sets.
● Training data. The training dataset contains a set of interactions between users and items. If a
user engages with an item, then there will be a record in the dataset.
● Test data. Each user is provided with a list with 100 candidates in the test dataset, you will need
to check the candidate list and recommend the top 10 items for each user.
● Validation data. Similar to the Test data, the difference is that the last column in the Validation
dataset is the ground truth of the ratings. You can use this dataset to tune your model.
Please train your recommender systems and generate the outputs for the test data.
Optional Data
In additional to the interaction data (training data), you are also provided with the following information:
1. User information.
2. Item information.
3. Social information.
Please note that the user, item, social information are optional data for this challenge. You don’t have to
use it. It is your own choice to determine if the information should be used or not.
More information can be found here
1. Participate in the challenge and make your submission. Maximum submission in Kaggle is
2 submissions per day.
2. This is an individual assignment. You have to finish it on your own.
3. In addition to the challenge, you have to finish a report on this challenge and submit it to
To Kaggle
● Kaggle submission, you need to submit your result on Kaggle.
To Moodle:
1. A csv file, “studentID.csv”. Please replace studentID as your own student ID. The content
should be the same as the file you have submitted to Kaggle. This file should be submitted
in Moodle. We will double check the files you have submitted to Kaggle and Moodle. If the
two files are not the same (i.e., the file submitted to Moodle cannot get the same score in
Kaggle), your result is invalid, and you will fail the assignment.
2. A jupyter notebook, “code_studentID.ipynb”. This notebook should show how you finish
the task. Ideally you should show what sort of algorithms you have considered, what kind of
information you have used, and the reason for your choice of the corresponding algorithm
to achieve the results you submitted to Kaggle. Comparison for different algorithms
should be included in this jupyter notebook. And detailed analysis of the results are
encouraged. The notebook should be self contained. If you have used other
algorithms/packages which are not covered in this lecture, you should give a detailed
introduction to that algorithm/package. If a third party package is used, this package should
be a well-known package and easy to install (e.g., install within a single command).
This nodebook should include both markdown explanation, codes, and outputs, so that we
can read and mark.
3. A pdf file, “code_stduentID.pdf”. This pdf is generated by cleaning all the output in the
jupyter notebook and exporting as a pdf file. This pdf will be passed in Turnitin for
plagiarism check.
4. A pdf report, “report_stduentID.pdf”. This pdf contains more detailed analysis of the work.
This pdf will be passed in Turnitin for plagiarism check.
● The kaggle leaderboard only shows your scores on 50% of the test data. Your final score
will be marked based on your csv file submitted to Moodle for the whole test dataset.
● The methodology and report for Task 1 is set to 30% of the total mark for this assignment,
and the prediction score accounts for 40% . So please prepare a good report and clearly
describe your method to achieve the task.
Task 2: Node Classification in Graphs (30% Marks)
You are given a graph, and you are required to perform the node classification in this graph
Dataset Description
You are given a citation network. In this network, each node is paper, an edge indicates the
relationship between two papers. As the network has extremely sparse network structure, we also
provide text information for each paper, i.e., the title of each paper. The files in the dataset include:
File Name Description
docs.txt title information of each node in a network,
each line represents a node (paper). The first
item in each line is the node ID
adjedges.txt neighbor nodes of each node in a network. The
first item in each line is the node ID, and the
rest items are nodes that have a link to the first
node. Node that if only one item in a line, it
means that the node has no links to other
labels.txt class labels of a node. Each line represents a
node id and its class label
The task is to perform the node classification for the papers presented in the labels.txt (The first
column is the node ID).


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