Making HPFanfiction Recommender

Haider Ali Punjabi
Jul 13 · 6 min read

Making HPFanfiction Recommender

Background

I have been reading Harry Potter Fan-fiction since late 2017. Around mid-2018, finding more stories became very difficult and I started finding story suggestions in the HPFanfiction subreddit. After a couple of months, a fic recommendation tool was posted in the subreddit. It was based on some previous research & data (I will link to all related stuff at the end of the post). It basically worked by comparing the user who likes two different fics and generating a similarity score. This tool (and its maker ElusiveGuy) was a huge inspiration behind my tool, and I initially hoped to use the same method but with a larger database (I have dabbled in the art of web scraping a bit).

Initial Plans

As I said, my initial plan (around December 2018) was to collect much more data than what ElusiveGuy’s tool was using. I knew fanfiction.net didn’t like scraping scripts, but I only wanted the metadata of the stories, not the stories themselves. I also planned on making a request every 2–3 seconds so that their server didn’t face any issues.

The problem with this plan was that there was no way of getting the list of users who like a particular story. You could only get the stories which a particular user liked (vir_innominatus, the guy who collected the data had a list of users from some previous analysis which he used). I might be able to pull this off now because I also have a huge list (7.5k +) of authors now.

I had to give up on this plan and shelved the project until inspiration struck.

Straightforwardness with the straightforward, crookedness with the crooked

In July 2019, inspiration struck while I was researching Reddit bots, reddit-ffn-bot specifically. It gave me an idea of a completely different approach to the problem. I know I couldn’t scrape fanfiction.net properly, but I could scrape Reddit. My new idea was to scrape the HPFanfiction subreddit and collect data on what people were recommending. This idea had merit, I knew, but I had to learn how to scrape Reddit properly.

PRAW

I had seen many projects use PRAW to work with Reddit. I also thought of doing the same, and it looked pretty easy. After making a basic scrapper using PRAW, I encountered a big problem. I could only access 1000 posts in the subreddit. It would have created a smaller database than what I had hoped for. Yes, it would have grown over time, but I didn’t like it. I kept the PRAW approach as a backup. I knew there were many huge databases of Reddit data, but filtering them for comments by one particular user (u/FanfictionBot) would have been difficult. In my search, I came across a gold mine of Reddit data.

pushshift.io

After browsing the internet for a while, I came across pushshift.io, which shows various Reddit related statistics along with a great API of a huge amount of Reddit Data. The API could give me sorted comments from u/FanfictionBot since June 2015. 4 years of data. This is what I was looking for. This API has great potential for all kinds of Reddit related projects. I am looking forward to using it in future as well.

Initial Scraping

The first step was to make a base database on my own computer before using a server to do it for small amounts of data regularly. I don’t have the worlds most stable internet, and I am also aware that the script could crash while scraping such a huge amount of data. I made another small script that downloaded all of the data so that I could work on local files. My initial download was 46 json files, around 120 megabytes of data. It took me a while to code a scraping algorithm that could work with all the template changes u/FanfictionBot has done over the years. I only focused on Story Title, Story URL, Author Name and Author Link initially. It took my computer around 3 hours to scrape 70k+ stories. After the database was made, I wrote some code which executed a given SQL command and exported its result to a JSON file. These JSON files would be used by the frontend.

Initially, I made the following JSON files:

  • Latest 1000 stories
  • Top 100 Stories (Past 7 days, Past 30 days, Past 365 days and all-time)
  • Top 100 Authors (Past 7 days, Past 30 days, Past 365 days and all-time)

Making the front-end

Choosing the right way to make the front-end was a rather difficult choice. I tried a lot of stuff (React, Vue, Hugo, to name a few) before settling on the simplest of all, HTML/CSS + JS/jQuery. I knew I didn’t need many elements, but a good amount of code. I am more comfortable in using jQuery than React. So, after the choice was made (4–5 hours), I started working on the front-end. It took me around 1.5 hours to make the website with Bulma CSS Framework. All the front-end code is available on Github.

Linking the back-end & front-end

This is probably where many people face problems. My own approach is somewhat unorthodox, as I haven’t seen anyone else doing this. I myself have been doing this since past 4–5 years. It’s kind of like a pseudo-JAMStack thing.

I hosted my script on PythonAnywhere. I am using its free plan, and it has been sufficient for all my projects. I have set-up a CRON job (1 per day on the free plan) that updates the database. It produces the set of JSON files I mentioned earlier and also triggers a Travis CI build of the front-end.

The TRAVIS CI build downloads the JSON files from the server and commits them to the repo. This way, the back-end is accessed only a couple of times a day (well within the limits of free plan) and the front-end (Netlify hosted) handles all the traffic (the largest on any of my projects yet).

In future, I am planning on increasing the CRON jobs each day, either by asking PA for them or by another trick I use on my other projects.

Updates after the first release

The initial release post on Reddit got a good number of views and lots of suggestions for improvements.

Adding metadata (story length, chapters,genre,rating,etc) was the biggest of them all. Writing a scraping algorithm for metadata proved to be extremely difficult due to the template changing over the years. I finally decided on scraping only the newer templates and scraped metadata for around 56% of the stories. It was a good solution as all the stories which didn’t get metadata were not linked by u/FanfictionBot for a long time.

After adding metadata, the next obvious update was to add filters based on it. Initially, I planned on making separate JSON files for every filter combination but the size of them was too large. I was forced to filter the data that the front-end was already using but I increase the number of stories & authors from 100 to 1000 to compensate. It worked perfectly.

Smaller updates were also made, which included:

  • Adding options to change the number of entries displayed in a page
  • Adding a progress bar & message when No Data is found

Deciding upon the best time for CRON job

To decide the best time for CRON job to start, I did some analysis and produced the following charts.

IMGUR Album of Charts

As you may see, 17:00 UTC is the best time for the CRON job, which I am currently using. I am planning to increase the daily amount of CRON jobs, using another nifty trick I know

Random problems I faced

  1. Too many requests for older versions of Font Awesome: I added Font Awesome to my project to add icons for metadata, but it slowed down the site by a huge margin. This Github Issue informed me that it’s a problem from their side, one I hope they resolve soon.
  2. Paired Characters not appearing: The u/FanfictionBot template is such that paired characters are listed in ‘<’ & ‘>’. This made the front-end HTML treat them as a tag name and created an element. To solve it, I used the following way:
    .replace(/</g, '&lt;').replace(/>/g, '&gt;')

References


Hackesta

Haider Ali Punjabi

Written by

Student | Developer | Photographer

Hackesta

Hackesta

Idea | Inspiration | Implementation

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