Meal Recommendation with Matrix Factorization
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Talk given at DSS-2016 on "Meal Recommendation with Matrix Factorization" -- Ashu Dubey 12 Labs
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Meal Recommendation with Matrix Factorization
- 1. Meal Recommendation using Turi Ashu Dubey @dubeyism Applause
- 2. Agenda The Need The Challenge The Solution The Result
- 3. The Need Recommending Healthy Meals to Users for Weight Loss Meal Should lead to weight-loss Should match users’ tastes & preferences Should recommend different meals for breakfast, lunch and dinner
- 5. The Solution Make recommendations for items, not meals: Break Meals into items like - (“Blueberry muffins and Omelette” ) to two items : (1) “Blueberry Muffins”, and (2) “Omelette”
- 6. (1) Separating Meals in Items Read the data meal = gl.SFrame.read_csv(‘meals.csv',delimiter=";") meal.rename({'X1': 'userid', 'X2': 'mealdescription','X3':'mealtype'}) Tokenize the meals
- 7. (1) Separating Meals in Items(contd..) Tokenize the meals mealRow=re.sub(")|(|d+|, | ,|,|+|.|@| - |- | -|*| [a-z] | w | and| n |and | and | with|with | with |/|; |;|:|%|=|^|#| "|| a | of | in | if | on |half|nothing| sm |null|glasses| glass","$", meal) p = re.compile( '$$$$$|$$$$|$$$|$$|$') # Split words separated by $ sign dict1 = p.split(meal)
- 8. (2) Split meals in Lunch, Dinner .. Basic SFrame filter based on meal category bmeal = meal.filter_by(['Breakfast'], 'mealtype')
- 9. (3) Creating the Model • bmodel = gl.ranking_factorization_recommender.create(newBreakfastFrame , target=‘frequency',ranking_regularization=0.5,max_iterations=500 ,num_factors=8) • bmodel_name = ‘breakfast_model' • bmodel.save(bmodel_name)
- 10. Item Similarity Recommender Factorization Recommenders (learns latent factors) Ranking Factorization Recommenders Popularity Based Recommender (4) Deciding between Recommenders
- 11. Can’t recommend unhealthy items like Cookies We need a classifier … (5) Healthy Meals? Healthiness Classifier
- 12. (6) Classifier
- 13. •Create Numpy Frames for Meals and Healthiness • Count Vectorizer (Experimented with ‘n’ n-grams) •Apply Logistic Regression on the Vectorized data (6) Logistic Classifier (SciKit)
- 14. •meal=np.genfromtxt("/Users/ashu/PycharmProjects/DataScience/ healthiness.csv",dtype=None,delimiter=";",comments=None,names= True,invalid_raise=False) •meal.dtype.names = (‘mealdesc','healthiness') • vectorizer = CountVectorizer(stop_words='english', ngram_range=(1, 3), dtype=‘double') •data = vectorizer.fit_transform(meal[‘mealdesc']) •clf = linear_model.LogisticRegression(C=1e5) •clf.fit(data,y) (6) Logistic Classifier (Implementation)
- 15. Get the Recommendations from Trained Model Find Healthiness of Recommended Items Filter Unhealthy Items (7) Finally..Recommendation
- 16. •recbfast = bmodel.recommend([client.userId],k=n,exclude_k nown=True) •avectorizer = Vectorize().get_vectorizer() •vectorizedmeals=avectorizer.transform(items) •healthiness = logclassifier.predict(vectorizedmeals) (7) Recommendation (contd..)
- 17. New data coming every second.. The data size soon became gigantic Refresh the old model, when new is ready (8) Deployment Challenges
- 18. The Result..in the beginning
- 19. The Result..after 2 days of data