Recommendation algorithms usually act like black boxes. I wanted to build a movie matchmaker that maps your mood to database tags without hiding the math.
I deployed the latest version on my side project. Getting the logic right forced me to solve a few ugly database problems.
Graceful degradation for SQL queries
Users demand hyper-specific things. They click sliders hoping for a 9.0+ rated 1980s sci-fi thriller about time travel.
A strict SQL query usually returns zero results for that. Blank screens kill engagement.
I built a graceful degradation loop into the PHP backend. If the strict query fails, the script strips the least important filters one by one. It drops the rating requirement first. If it still finds nothing, it drops the release year. It keeps the core mood intact, but widens the net. The user always gets a recommendation.
Honest match percentages
I wrote a strict scoring function to calculate the match percentage you see on the screen.
The script counts the exact number of requested criteria. If you want a specific tag, a specific genre, and a specific decade, that equals three points.
The algorithm divides the met criteria by the total requested criteria. If the fallback loop had to drop the decade filter to find a movie, your match drops to 66 percent. The UI reflects exactly how close the suggestion is to your original prompt.
Caching the heavy lifting
Counting tag intersections across thousands of movies slows down the server.
I tied the genre aggregations to WordPress transients. The script recalculates the tag clusters every six hours. It dumps the results into memory. The frontend pulls directly from the cache. The UI feels instant now.
The live build
You can poke around the production build and test the matching logic at Movievia.com. Hit the mood generator and see how the percentages adjust.
I am currently trying to figure out how to weight user watchlists into the score without blowing up the query time.
How do you handle complex tag-based filtering without writing massive SQL joins?