Shelf-Help Book Recommender

Like self-help, but with books. For your mental bookshelf.

This project is a command-line book recommendation system designed to practice core data structures and algorithms using Python. The main goal is not to build a real recommender engine, but to apply fundamental concepts like linked lists, merge sort, and binary search in a realistic scenario using book data.

Main Learning Goals

• Understand, implement and use custom linked lists
• Practice merge sort on lists of dictionaries
• Implement binary search for strings
• Design a clean and modular object-oriented structure

Class Documentation

LinkedList (in models/linked_list.py)

Custom implementation of a linked list to store and manage book dictionaries.

Features:

• append(book): Adds a new book node to the end of the list
• display_all(): Prints all books in the list
• get_books_data(data): Returns a list of values from each book for a specific key (e.g. 'title')
• get_all_books(): Returns all stored book dictionaries in the linked list

BookAlgorithms (in models/book_algorithms.py)

A class providing sorting and searching operations on a list of dictionaries (e.g., book data).

Features:

• merge_sort_books(): Sorts the list using merge sort by the selected key (e.g. score or title)
• binary_search_inside_list(searching_element): Performs binary search on the sorted list by the given key. Works with strings and numbers.

To-Do List

• Implement binary_search_inside_linkedlist() function inside BookAlgotithms class
• Handle case-insensitive string comparisons (e.g. lower())
• Add CLI prompts to interact with user preferences (genre, mood)
• Implement quick sort for comparison
• Add exception handling and input validation
• Use Book class object instead of raw dictionaries

Manual Setup

If you prefer manually setting up the environment, follow these steps:

1. Clone the repository: git clone https://github.com/AnzelaMachackova/shelf-help-book-recommender
2. Create a virtual environment: python3 -m venv venv
3. Activate the virtual environment: source venv/bin/activate
4. Run the main script: python3 main.py with the repositories.