Cocktail Recommentation Engine: Part 3 - The First Results

The following table is a subset of the output results from the cocktail recommendation engine (be warned some rude words in some of the cocktail names). I have tried to drill the list down to just the best matches so bear in mind there are probably 30% more matches thrown out which appear to have statistical significance but I can find no reason for the relationship (beyond sometimes they are next to each other in the large cocktail A-Z list).

Cocktail One - the cocktail someone is on, Cocktail Two - the cocktail I recommend, Probability - the probability that if someone is on cocktail one and visits another cocktail cocktail two will be that cocktail. There had to be at least 10 relationships in total recorded between cocktail one and another cocktail in my db for a cocktail to make this table.

I am hoping that as I get more data this table will grow more accurate and I will have fewer "wtf?" moments looking at the list. I also want to add two more probabilities to the calculation:

1. Probability if they visit 1 they will like 2
2. Probability if I display 2 on 1 they will click on it

Anyway for now enjoy looking at the cocktail relationships and expect more data soon.

Cocktail One Cocktail two Prob. The Refresher Tom Collins 0.76 throat cramp Toxic Waste 0.67 Raunchy island sex Red Headed Slut 0.5 three wise men Three Wisemen 0.5 Tomate Momisette 0.43 Wicked one Screwdriver 0.35 Amaretto Rose Amaretto Dream 0.33 Purple Haze Radiation 0.33 Strawberry shortcake StrawBerry Shortcake 0.33 Toxic Waste Titanic Sinkers 0.32 martini spiller Niks Shot of Aussie 0.32 The Orville three wise men 0.31 3 Headed Parrot A-Bomb 0.3 BABY GUINNESS Baby Guinness 0.3 Flaming Shooter Flatliner 0.29 tequila slammer!!!!!!!!!!! TEQUILA SUICIDE ! ! ! ! ! ! ! ! ! ! ! 0.28 see you at the hospital Tequila Sunrise 0.28 Absolut Nut Absolut Quaalude 0.27 Sex on the beach Sex On The Beach 0.27 suicide surfer on acid 0.26 Tom Collins Orange Fizz 0.24 Kick in the Willy martini spiller 0.24 Slippery Dick Slippery Nipple 0.24 Multiple Orgasm Multiple Screaming Orgasm 0.24 Absolut Stress absolutly mistafying 0.22 death cock sucking cowboy 0.22 A strawberry with milk A-Bomb 0.22 Cheeky Vimto Layered cheeky Vimto 0.21 Tequila Sunrise throat cramp 0.21 Sex On The Beach Sex on the beach 0.19 Screwdriver Bay Breeze 0.19 Arsonist awamawama ding dong 0.19 Absinthe Drinker Absinthe Minded 0.19 nipple twister Nymphomaniac 0.19 Jolly Green Giant Flaming Lamborghini 0.17 Flatliner Flaming Lamborghini 0.17 Zipper Zombie 0.17 Absolut Nut Level 28 0.17 Absinthe Drinker Royal Absinthe Fizz 0.17 Long island ice tea Long Island Iced tea 0.16 Slippery Dick cock sucking cowboy 0.14 blowjob Blow Job 0.14 Blow Job Clit Licking Cowgirl 0.14 Wet Pussy Wet Dream 0.13 Pina Colada Pina Colada (Virgin) 0.13 Screaming Orgasm Sex on the beach 0.13 Orgasm Cocktail Screaming Orgasm 0.13 B52 Blow Job 0.13 Slippery Nipple cock sucking cowboy 0.13 Sex on the beach (shooter) Sex on the beach 0.13 Black Russian Black Russian - proper 0.13 Brain Damage brain haemorage 0.12 Alabama Slamma Alabama Slammer 0.12 cock sucking cowboy Clit Licking Cowgirl 0.12 Mai Tai Mojito 0.12 Sex on the beach Slippery Nipple 0.12 Pornstar Purple rain 0.12 Pornstar Quick Fuck 0.12 Baby Guinness Bad Babysitter 0.11 Arsonist red , white & Blue 0.11 Baby Guinness A-Bomb 0.11 Baby Guinness Billy Badass 0.11 Screwdriver Wicked one 0.11 Angel's Kiss B52 0.1 Blow Job blowjob 0.1 Clit Licking Cowgirl A-Bomb 0.1 Clit Licking Cowgirl cock sucking cowboy 0.1 Flaming Lamborghini Deep Throat 0.09 A-Bomb Abbey cocktail 0.09 Sex On The Beach Sex on the beach (shooter) 0.09 Blue Lagoon blue lagoon special 0.08 Slippery Nipple Sex on the beach 0.08 Clit Licking Cowgirl Orgasm Cocktail 0.08 Clit Licking Cowgirl Deep Throat 0.08 Screaming Orgasm Sex On The Beach 0.08 B52 Baby Guinness 0.08 Screaming Orgasm Blow Job 0.07 Blow Job Blue Lagoon 0.07

Comments

[Alex Schultz]

Re. the alphabetical bias although in the rest of the data it is also strong, in the data above I wouldn't say it is mindblowing. The reason I say that is relationships such as "Slippery Dick Slippery Nipple " are to be expected and similarly with the vast majority of the other cocktails on the list above with the same few starting letters.

I will certainly agree with you that I need to look at CTR once I add the recommendations (but I do need a starting point with which to produce the recommendation list) so probability of click will be one key factor (as pointed out in the article above) but I think probability of liking will be a key factor too (i.e. if I view cocktail a and then visit cocktail b what is the average rating b is given in that scenario).

I definitely want to do something with a chain of cocktails (i.e. if I saw a and then b what is the most likely cocktail next) but baby steps first baby steps :).

[NatC]

...this looks like a huge alphabetical bias.

I would recommand developping a method that, on each page, would pick up and list a few "Suggestions", let's say five. The first three would be those that have the best clickthrough rate from the current cocktail page, the two others would be chosen at random (in order to be able to account for new cocktails or new trends in user interests).

This way you do not have anything to manage once it's functional. You just need to log in how many times you displayed each suggestion on any given cocktail page, and how many times it's been clicked on.

The next stage would then be to build session profiles, where you do not just take into account the cocktail currently viewed, but also those browsed through during the same session. These could make up a "pool" of cocktails, from which - based on the individual best matches - you could compile the best suggestions.

Would that make sense?