1. Recommender Systems and Fast Algorithms
By: Souparni Agnihotri, Omar Taylor,
Charlie Hubbard and Chinmay Hegde

2. What are Recommender Systems?
Information Filtering Systems
Gives recommendations based on
user preferences and on user/item
Information. .
** We can make recommendations not only based on user prefrences but also on user/item information (content-based) **

3. They are everywhere! Amazon, LinkedIn, Twitter...
Twitter -> depending on the types of people you follow, it recommends the types of people we would like

4. Challenges Scalability Accuracy of Recommendations
Scalabitlity - N users and M items mean N*M possible recommendations!
Accuracy - accuracy and ranking can be tied into the same thing

5. POTLUCK!
End-to-end recipe application that builds on a recommender system. Utilized spoonacular as a means of gathering recipe dataset.
Spoonacular is an open source API
MongoDB database
NodeJS server
Javascript and HTML to build UI
Android app
Firebase to structure the database.
Android studio to build UI

6. DATABASE User User Recipes Item Info Rating Future Data CLIENT
Web App/
Android app
**Include user-item ratings in the storage somewhere!**

7. Explain the process in which the JSON is generated

9. Machine Learning Concepts
CONTENT BASED FILTERING
COLLABORATIVE FILTERING
We have used content based filtering to measure similarities
Content Based Filtering:
Makes prediction based on static information provided by the user/ information that has already been provided to us.
Eg:
Consider ingredients of different recipes and compare them to find similar ones.
Collaborative filtering:
Similarities between items are calculated from ratings matrix and based upon these similarities, user preference for an item not rated by him is calculated.
Eg: Getting several users ratings on different recipes and predicting one user’s rating on a particular recipe.

10. Making Recommendations

11. Results
Some outputs from similarity measures:

12. PotLuck Demo

13. Summary and future work
Mongo DB, Node JS, HTML
TFIDF, cosine similarity
GPUFish - Hybrid filtering model (Content + Collaborative)
References:
C. Hubbard, C. Hegde. “GPUFish: A Parallel Computing Framework for Matrix Completion from a few observations”. Iowa State University Technical Report. November 2016
“Food and Recipe API.” Spoonacular. spoonacular.com/food-api
C. Hegde. “Modeling Data in High Dimensions”. EE 525X. Iowa State University, March 2017
Mongo DB database, Node JS server and Java sctipt and HTML used to develop app.
GPUFish is a parallel computing framework to solve very large scale matrix completion problems.
Extremely beneficial for large scale content providers like
Make use of a hybrid filtering model (Content + Collaborative) called GPUFish to increase the accuracy of our predictions.
Netflix and Amazon to filter their data in a fast and efficient manner and get accurate results.

14. Content Based Filtering
TFIDF(Term Frequency Inverse Document Frequency)
TF : Refers to the frequency of a word in a document
IDF : Inverse of the document frequency amongst the whole set of documents.

15. Similarity measures
Cosine Similarity: Measures the similarities between two different documents. In our case, compare one ingredient list of a recipe with the ingredients of another recipe.
Ta and Tb are the vectors that represent the documents.
Jaccard Similarity: Measures similarities between two sets
Sa and Sb are the sets containing the terms that appear in two different documents