NLP Recommendation Engine Report Writing - IT Assignment Help

Assignment Task:

Task:

Overview
Natural language processing (NLP) is commonly used to build recommendation engines. This assignment involves building reading recommendation engines for students in Australian higher education based on a sample of subject reading lists sourced from public sites.

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.

Learning outcomes
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.
 

Background
Currently University lectures rely on librarians to do background research on relevant, compliant, and current reading material suitable for course teaching units. 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 is not possible yet in Australia as no up to date central repository exists for reading list material.
The ability to recommend reading material down to a book and page level and at a journal level would be a useful product for academics, publishers and agencies supporting higher education.

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