CS代写 IFN647 – Assignment 2 Requirements Weighting: 35% of the assessment for IFN – cscodehelp代写

IFN647 – Assignment 2 Requirements Weighting: 35% of the assessment for IFN647
Deliverable Items
1. A final report in PDF or word file, which includes
• Statement of completeness and your name(s) and student ID(s) in a cover

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• Your solution for all steps (See more details in the “Requirements for each
step” session).
• User manual section describing the structure of the data folder setup,
information on “importing packages” and how to execute Python code in
Terminal, IDLE or PyCharm; and
• An appendix listing the top 10 documents for all topics and all models.
2. Your source code for all steps, containing all .py files (using a zip file “code.zip” to put them together) necessary to run the solution and perform the evaluation (source code only, no executables); and
3. A poster and demo to your tutor during your week 13 workshop time.
Please zip all files (the final report, code, and poster) into a zip file as your “studentID_Asm2.zip” and submit it through the Blackboard (a single submission only for a group) before the due date.
Please do not include the dataset folder generated by “DataCollection.zip” or “RelevanceFeedback.zip” in your submission.
The following are the frameworks/libraries that you can used for assignment 2: (a) sk-learn
(c) pandas
(e) Matplotlib
If you want to use another package or library, you need to get your tutor’s approval. Due date of Blackboard Submission: Sunday week 13 (12th June 2022)
Group: You are required to work on this assignment in a group of three people.

Due to the difficulty of automatically obtaining user information requirements, most search systems only use queries (or topics) instead of user information requirements. The first reason is that users may not know how to represent the topics they are interested in. The second reason is that users may not want to spend a lot of effort mining relevant documents from the hundreds of thousands of candidates provided by the system.
This unit discusses several ways to extend query-based methods, such as pseudo-relevant feedback, query expansion or hybrid methods. In this assignment, for a given data collection (including 50 datasets), you need to discover a good information filtering model that recommends relevant documents to users on all 50 topics, where documents in each dataset are collected for a corresponding topic. The methodology you will use for this assignment includes the following steps:
• Step 1 – Design an IR-based baseline model (BM_IR) that ranks documents in each dataset using the corresponding queries for all 50 datasets.
• Step 2 – Based on the knowledge you gained from this unit, design two different models to rank documents in each dataset using corresponding queries from all 50 datasets. You can call them Model_1 and Model_2, respectively.
• Step 3 – Use python to implement three models: BM_IR, Model_1 and Model_2, and test them on the given data collection of 50 topics (50 datasets) and print out the top 10 documents for each dataset (put the output in the appendix of your final report).
• Step 4 – Choose three effectiveness measures to display testing results against the selected effectiveness measures in tables or graphs.
• Step 5 – Recommend the best model based on significance test and your analysis. Data Collection
It is a subset of RCV1 data collection. It is only for IFN647 students who will be supervised by Prof. Li Yuefeng. Please do not release this data collection to others.
DataCollection.zip file – It includes 50 Datasets (folders “dataset101” to “dataset150”) for topic R101 to topic R150.
“Topics.txt” file – It contains definitions for 50 topics (numbered from R101 to R150) for 50 datasets in the data collection, where each element (…) defines a topic, including topic number (), title (