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MA5851 Data Science
Questions:
Natural language processing (NLP) is commonly used to build recommendation engines This assignment involves building reading recommendation engines for teachers in Australian schools based on textbooks lists sourced from public websites. The two main types of NLP reading recommendations models that are generally applicable to the reading list recommendation problem are:
Content-based filters — use item metadata (description, rating, products features, reviews, tags, genres) to find items like those the user has enjoyed in the past.
Collaborative filtering — Collaborative filtering systems analyse users’ interactions with the items (e.g. through ratings, likes or clicks) to create the recommendations.
Understand and apply new data science skills, knowledge, and techniques to solve problems in data science using natural language processing (NLP). Work-based skills The ability to automatically build labelled datasets and map text hierarchies using NLP is valuable back-office automation opportunity saving time and increasing accuracy for organisations.
Currently s chools typically rely on the same publisher for textbooks. When the curriculum changes, then a school typically uses the same publisher for an updated textbook. Changing publishers requires considerable investment by school staff. One difficulty in changing textbooks is keeping appraised on different publishers updated textbooks which may be suitable for a school subject and year level.
In Europe, it is possible to use existing and past reading list information from different University’s to inform academics, librarians and publishers about comparative reading that might be applicable to any selected topic. This type of reading recommendation system could be transferred to school textbooks so that a school could be informed of alternative textbooks that are suitable. Such a school textbook recommender would assist teachers choose new textbooks.
The data to be used as the initial starting point for the Tasks is given in the Assessment 1 folder on Learn JCU. A summary of the variables is given in Table 1. The provided data is insufficient to develop NLP recommenders or provide assessments of NLP recommender quality.
1. Develop two NLP recommendation engines derived on the University’s reading list material (supplied data) and apply the NLP recommenders to one of the following options:
a. Recommend existing textbooks to similar schools | subjects | year level, or
b. Recommend new textbooks to existing schools | subjects | year level
2. Determine the quality of the two NLP recommenders from Task 1 using test and training sets derived from the supplied data.
3. Compare the two NLP recommenders.
A written report is to be provided incorporating all three tasks. The report must show comprehensive thought of your decisions, clearly communicate your ideas, and linked to NLP theory/applications with appropriate references. You will need to include figures, tables, and code sections in your report. Figures, tables and code sections do not contribute to the word count. Code sections must be presented as text, screen capture of code will not be graded. All figures, tables and code must have a caption and be referenced in your discussions using the appropriate captions. Figures, tables, and code sections that are not referenced in discussions do not contribute to grading.
The provided data will require:
Creation of a labelled dataset from the source data, and Supplementation of the dataset from at least one external resource (API).
MA5851 Data Science
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