Fantastic Books and Where To Find Them

An expert system is one of the first successful forms of AI and was popular in the 80's (yes, pre-Python and Java design patterns, but after the start of great ML and AI academic research). This expert system uses a rule engine written in late 1995 by an engineer at Sandia National Labs.

The rule engine itself is based on an implementation of the Rete algorithm, which optimizes on a simple looping through conditionals by implementing a trie of left hand side patterns to match, and marking nodes as they are fulfilled (not necessarily in sequential order). When a leaf node is reached, the corresponding rule is fired.

Dependencies

• Jess rule engine, Java-based, can integrate with JSR94 rule engine API
• CLIPS functional programming language
• Maven build system, install dependency by downloading jess.jar, running mvn install, referencing under a <dependency> tag in pom.xml

Functionality

• Asks series of questions based on characteristics of books in dataset
• Guaranteed recommendation when one remaining book that fits user written characteristics in dataset
• Uses a CLI and string-built questions

How to Run

This expert system is currently standalone, so this is the process to run the CLI recommendation system. A future improvement is to write a driver to run the code using Java then package into a .jar file.

1. Download jess.exe from https://www.jessrules.com/jess/download.shtml. (Fun fact: I got to interact with the Jess creator during this process).
2. Save book_recs.clp in the examples/jess folder.
3. Run jess.exe using bin/jess.
4. Run (batch "examples/jess/book_recs.clp").

Future Improvements

This program is not actively worked on at the moment, but forks and pull requests are certainly welcome. The following example extensions are not time consuming to implement.

• Move build to Maven and write Java driver.
• Add length of book as a characteristic.
• Make it so that if the first question is given a book the system doesn't know, it saves that book into the database.
• Standardize which characteristics go with which appeal factor.
• Output the reason why the book was chosen along with the recommendation.

Easy Contributions

• Add a book! Genres can be verified by looking at the Wikipedia page of the book.
• Update the characteristics of a book.