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Fast recipe browsing for maximal inspiration
Thatsfordinner.com
Alexander Jerneck
Demo
Data
● 16K recipes scraped from epicurious.com and allrecipes.com.
● Ingredient and title words are the features.
● Typical NLP preprocessing and feature selection.
● Vocabulary size: about 1800 words.
Algorithm: Topic model (Latent Dirichlet Allocation)
Potatoes 0.7
Russet 0.1
Gold 0.1
Yukon 0.1
Chili 0.4
Cilantro 0.4
Avocado 0.2
Ginger 0.4
Rice 0.3
Soy 0.3
● Documents are mixtures of topics and words in documents are drawn
from topics:
○ 1 tsp soy sauce, 1 cup rice, 1 piece ginger, 1 chili pepper
○ 3 pounds russet potatoes, 3 pounds Yukon Gold potatoes, 1 stick butter, 1 cup whole
milk, salt
○ 1 avocado, 1 tbsp cilantro, 2 tbsp onion, 1 chili pepper
How many
topics?
Validation:
Stability
Alexander Jerneck
● Programming enthusiast living inside a social scientist (Ph.D., Sociology).
● I did this project because
○ I want to be able to read everything.
○ I want to make people find new things they didn’t know they wanted.
LDA: Generative process
1. For each topic,
a. Draw a distribution over words βk
∼ DirV
(η).
2. For each document,
a. Draw a vector of topic proportions θd
∼ Dir (α).
b. For each word,
i. Draw a topic assignment Zd,n
∼ Mult(θd
), Zd,n
∈ {1,..., K}.
ii. Draw a word Wd,n
∼ Mult(βzd,n
),Wd,n
∈{1,...,V}
V is the size of the vocabulary.
K is the number of topics.
η and α are hyperparameters determining the shape of the Dirichlet distributions.
.
LDA: Inference
The posterior:
p(θ1:D
,z1:D,1:N
,β1:K
|w1:D,1:N
,α,η) = p(θ1:D
,z1:D
,β1:K
|w1:D
,α,η)/
∫β1:K
∫θ1:D
∑z
p(θ1:D
,z1:D
,β1:K
|w1:D
,α,η)
is intractable because of the integral in the denominator.
I use collapsed Gibbs sampling, implemented in the python lda package to sample from the
posterior.
Landing page: all categories
Category detail view
All recipes in a category
Search for lemon + butter in a category

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Thatsfordinner demo

  • 1. Fast recipe browsing for maximal inspiration Thatsfordinner.com Alexander Jerneck
  • 3. Data ● 16K recipes scraped from epicurious.com and allrecipes.com. ● Ingredient and title words are the features. ● Typical NLP preprocessing and feature selection. ● Vocabulary size: about 1800 words.
  • 4. Algorithm: Topic model (Latent Dirichlet Allocation) Potatoes 0.7 Russet 0.1 Gold 0.1 Yukon 0.1 Chili 0.4 Cilantro 0.4 Avocado 0.2 Ginger 0.4 Rice 0.3 Soy 0.3 ● Documents are mixtures of topics and words in documents are drawn from topics: ○ 1 tsp soy sauce, 1 cup rice, 1 piece ginger, 1 chili pepper ○ 3 pounds russet potatoes, 3 pounds Yukon Gold potatoes, 1 stick butter, 1 cup whole milk, salt ○ 1 avocado, 1 tbsp cilantro, 2 tbsp onion, 1 chili pepper
  • 7. Alexander Jerneck ● Programming enthusiast living inside a social scientist (Ph.D., Sociology). ● I did this project because ○ I want to be able to read everything. ○ I want to make people find new things they didn’t know they wanted.
  • 8.
  • 9. LDA: Generative process 1. For each topic, a. Draw a distribution over words βk ∼ DirV (η). 2. For each document, a. Draw a vector of topic proportions θd ∼ Dir (α). b. For each word, i. Draw a topic assignment Zd,n ∼ Mult(θd ), Zd,n ∈ {1,..., K}. ii. Draw a word Wd,n ∼ Mult(βzd,n ),Wd,n ∈{1,...,V} V is the size of the vocabulary. K is the number of topics. η and α are hyperparameters determining the shape of the Dirichlet distributions. .
  • 10. LDA: Inference The posterior: p(θ1:D ,z1:D,1:N ,β1:K |w1:D,1:N ,α,η) = p(θ1:D ,z1:D ,β1:K |w1:D ,α,η)/ ∫β1:K ∫θ1:D ∑z p(θ1:D ,z1:D ,β1:K |w1:D ,α,η) is intractable because of the integral in the denominator. I use collapsed Gibbs sampling, implemented in the python lda package to sample from the posterior.
  • 11. Landing page: all categories
  • 13. All recipes in a category
  • 14. Search for lemon + butter in a category