Downloaded 31 times
![27
What are industrial companies doing?
“Netflix member loses interest after
perhaps 60 to 90 seconds of choosing”
[source]
“Netflix recommender system is used on most
screens of the Netflix product beyond the
homepage, and in total influences choice for
about 80% of hours streamed at Netflix.”
[source]
“Already, 35 percent of what consumers purchase on Amazon
and 75 percent of what they watch on Netflix come from
product recommendations based on such algorithms.”
[source]](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-27-2048.jpg)
![43
Matrix factorization – MovieLens example
Read files
import csv
movies_fname = '/path/ml-latest/movies.csv'
with open(movies_fname) as fobj:
movies = dict((r[0], r[1]) for r in csv.reader(fobj))
ratings_fname = ’/path/ml-latest/ratings.csv'
with open(ratings_fname) as fobj:
header = fobj.next()
ratings = [(r[0], movies[r[1]], float(r[2])) for r in csv.reader(fobj)]
Build sparse matrix
import scipy.sparse as sp
user_idx, item_idx = {}, {}
data, rows, cols = [], [], []
for u, i, s in ratings:
rows.append(user_idx.setdefault(u, len(user_idx)))
cols.append(item_idx.setdefault(i, len(item_idx)))
data.append(s)
ratings = sp.csr_matrix((data, (rows, cols)))
reverse_item_idx = dict((v, k) for k, v in item_idx.iteritems())
reverse_user_idx = dict((v, k) for k, v in user_idx.iteritems())](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-43-2048.jpg)
![44
Matrix factorization – MovieLens example
Fit Non-negative Matrix Factorization
from sklearn.decomposition import NMF
nmf = NMF(n_components=50)
user_mat = nmf.fit_transform(ratings)
item_mat = nmf.components_
Plot results
component_ind = 3
component = [(reverse_item_idx[i], s)
for i, s in enumerate(item_mat[component_ind ,
:]) if s>0.] For movie, score in sorted(component,
key=lambda x: x[1], reverse=True)[:10]:
print movie, round(score)
Terminator 2: Judgment Day (1991) 24.0
Terminator, The (1984) 23.0
Die Hard (198 19.0
Aliens (1986) 17.0
Alien (1979) 16.0
Exorcist, The (1973) 8.0
Halloween (197 7.0
Nightmare on Elm Street, A (1984) 7.0
Shining, The (1980) 7.0
Carrie (1976) 7.0
Star Trek II: The Wrath of Khan (1982) 10.0
Star Trek: First Contact (1996) 10.0
Star Trek IV: The Voyage Home (1986) 9.0
Contact (1997) 8.0
Star Trek VI: The Undiscovered Country (1991) 8.0
Blade Runner (1982) 8.0](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-44-2048.jpg)
![47
Example of feature: Named entities in product description
Sample of code with Polyglot
from polyglot.text import Text
text = Text(blob)
for sent in text.sentences:
print(sent, "n")
for entity in sent.entities:
print(entity.tag, entity)
(Sentence("A New York, au printemps 2008, alors que l'Amérique bruisse des prémices de l'élection présidentielle,
Marcus Goldman, jeune écrivain à succès, est dans..."), 'n')
(u'I-LOC', I-LOC([u'New', u'York']))
(u'I-PER', I-PER([u'Marcus', u'Goldman']))
(Sentence("Lire la suite la tourmente : il est incapable d'écrire le nouveau roman qu'il doit remettre à son éditeur
d'ici quelques mois."), 'n')
(Sentence("Le délai est près d'expirer quand soudain tout bascule pour lui : son ami et ancien professeur
d'université, Harry Quebert, l'un des écrivains les plus respectés du pays, est rattrapé par son passé et se retrouve
accusé d'avoir assassiné, en 1975, Nola Kellergan, une jeune fille de 15 ans, avec qui il aurait eu une liaison."),
'n')
(u'I-PER', I-PER([u'Harry', u'Quebert']))
(u'I-PER', I-PER([u'Nola', u'Kellergan']))
https://fr.shopping.rakuten.com/mfp/3011174/la-verite-sur-l-affaire-harry-quebert-joel-dicker-livre?pid=171972011 2018/11/05](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-47-2048.jpg)
![50
Word2vec: Code sample
import string
import unidecode
import requests
from bs4 import BeautifulSoup
refs = ["annee1", "artgrdp1", "ballades1", "bugjarg1", "contemplA2",
"contemplB2”, "feuilles1", "hugoshak1", "legend1", "legendet21",
"nddp1", "oriental1”, "quatrevt1", "rayons1", "ruesboi1", "satan1"]
textes = ""
for ref in refs:
print ref
res = requests.get("http://abu.cnam.fr/cgi-bin/donner_html?%s" %
ref)
text = BeautifulSoup(res.content, 'lxml').text
textes += text.split("DEBUT DU FICHIER")[1].split("FIN DU
FICHIER")[0]
sentences = [t for t in textes.replace("n", " ").replace("r", "
").split(".") if len(t) > 20]
sentences = [t.lower().strip() for sentence in sentences for t in
sentence.split(";")]
words = [[t.strip().strip(string.punctuation) for t in
sentence.split()] for sentence in sentences]
words = [[unidecode.unidecode(t) for t in word] for word in words]
Model learning with Gensim
import gensim
model = gensim.models.Word2Vec(words, min_count=5, size=500, sg=1)](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-50-2048.jpg)
![51
Word2vec: two-layer neural network
We can use + and – operations on embedding vectors to find logical relationships
model.most_similar("femme”)
[('fille', 0.8448578715324402),
('danseuse', 0.8413887023925781),
('heureuse', 0.8404737114906311),
('fee', 0.8399657011032104),
('jolie', 0.8360834717750549),
('epouse', 0.8242064714431763),
('malheureuse', 0.823990523815155),
('jeune', 0.8126694560050964),
('creature', 0.8117369413375854),
('bohemienne', 0.8117284178733826)]
model.most_similar(positive=["quasimodo", "femme"], negative=["homme"])
[('bohemienne', 0.7697243094444275),
("l'egyptienne", 0.7591158747673035),
('chevre', 0.7411096096038818),
('recluse', 0.7373903393745422),
('esmeralda', 0.733445405960083),
('rene-jean', 0.7310534715652466),
('parole', 0.7230771780014038),
('tourna', 0.715079665184021),
('vivement', 0.7149174213409424),
('condamnee', 0.7142783403396606)]
model.most_similar("philosophe“)
[('ecolier', 0.9446097016334534),
('soulier', 0.9436287879943848),
('bandit', 0.9423332810401917),
('bonhomme', 0.9420523643493652),
('ennemi', 0.938879132270813),
('gentilhomme', 0.9362510442733765),
('savant', 0.9335594177246094),
('officier', 0.9284510612487793),
('traitre', 0.9282448887825012),
('damne', 0.926963210105896)]](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-51-2048.jpg)
![57
Prod2vec: Python example with Gensim
Model learning with Gensim
from gensim.models import Word2Vec
fobj = open(’purchases.json')
model = Word2Vec(fobj, size=100, window=5, min_count=1, workers=4)
embeddings = model.wv.syn0
indices = model.wv.index2word
KNN computation with Falconn
import falconn
params_cp = falconn.get_default_parameters(embeddings.shape[0], embeddings.shape[1])
params_cp.lsh_family = falconn.LSHFamily.CrossPolytope
params_cp.distance_function = falconn.DistanceFunction.EuclideanSquared
lsh = falconn.LSHIndex(params_cp)
lsh.setup(embeddings)
query = lsh.construct_query_object()
for i in range(embeddings.shape[0]):
res = query.find_k_nearest_neighbors(embeddings[i], knn)](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-57-2048.jpg)
![94
Baseline code example
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import linear_model
from sklearn.cross_validation import KFold
STOPWORDS = set(['alors', 'au', 'aucuns', 'aussi', 'autre', 'avant', 'avec’ …])
fname = '/path/to/reviews_test_clean 2.csv’
df = pd.read_csv(fname)
df['feedback_ratio'] = df['feedback_positive_count'].astype(float) / (df['feedback_positive_count’] +
df['feedback_negative_count'])
df['feedback_class'] = df['feedback_ratio'] >= 0.5
X = df['review_content'].values
Y = df['feedback_class'].to_dense().values
kf = KFold(n=len(X), n_folds=5)
all_pred , all_test = [], []
for train, test in kf:
Xtrain, Xtest = X[train], X[test]
Ytrain, Ytest = Y[train], Y[test]
vect = TfidfVectorizer(stop_words=STOPWORDS)
Xtrain = vect.fit_transform(Xtrain)
Xtest = vect.transform(Xtest)
clf = linear_model.SGDClassifier()
clf.fit(Xtrain, Ytrain)
pred = clf.predict(Xtest)
all_pred.extend(pred)
all_test.extend(Ytest)](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-94-2048.jpg)
![100
Baseline code example
from collections import defaultdict
import pandas as pd
import numpy as np
import sklearn.preprocessing
from scipy.sparse import hstack
xtrain_df = pd.read_csv('training_X.tsv’,
delimiter='t’)
xtest_df = pd.read_csv('test_X.tsv’, delimiter='t’)
ytrain_df = pd.read_csv('training_Y.tsv’,
delimiter='t’)
ytest_df = pd.read_csv('test_Y.tsv’, delimiter='t’)
CATS = ['WARRANTIES_FLG’, `'SHIPPING_MODE`, …]
LABELS = defaultdict(set)
for cat in CATS:
d =
set(xtrain_df[cat].unique()).union(set(xtest_df[cat].un
ique()))
LABELS[cat] = dict((v, i) for i, v in enumerate(d))
NUMERICAL = ['CARD_PAYMENT’, COUPON_PAYMENT’,…]
ENCODERS = dict()
COLUMNS = []
Xtrain = create_matrix(xtrain_df, COLUMNS)
Xtest = create_matrix(xtest_df)
def create_matrix(df, COLUMNS=None):
print df.shape
X = df[NUMERICAL].to_sparse()
if COLUMNS is not None:
COLUMNS.extend(NUMERICAL)
for cat in CATS:
if COLUMNS is not None:
for v in sorted(LABELS[cat].iteritems(),
key=lambda x: x[1]):
COLUMNS.append('%s (%s)' % (cat, v[0]))
data = df[cat]
data = np.ravel([LABELS[cat][v] for v in data])
data = np.reshape(data, [data.size, 1])
if cat in ENCODERS:
data = ENCODERS[cat].transform(data)
else:
oenc = sklearn.preprocessing.OneHotEncoder()
data = oenc.fit_transform(data)
ENCODERS[cat] = oenc
X = hstack((X, data))
return X](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-100-2048.jpg)
![101
Baseline code example
lenc = sklearn.preprocessing.LabelEncoder()
Ytrain =
lenc.fit_transform(ytrain_df['CLAIM_TYPE'])
Ytrain[Ytrain != 0] = 1
lenc = sklearn.preprocessing.LabelEncoder()
Ytest = lenc.fit_transform(ytest_df['CLAIM_TYPE'])
Ytest[Ytest != 0] = 1
from sklearn.ensemble import RandomForestClassifier
clf =
sklearn.ensemble.RandomForestClassifier(n_estimator
s=50)
clf.fit(Xtrain.toarray(), Ytrain)
pred = clf.predict(Xtest.toarray())
ytrain_df['CLAIM_TYPE'][ytrain_df['CLAIM_TYPE'] == '-'] =
'NO COMPLAIN'
ytest_df['CLAIM_TYPE'][ytest_df['CLAIM_TYPE'] == '-'] = 'NO
COMPLAIN’
STATUS =
set(ytrain_df['CLAIM_TYPE']).union(set(ytest_df['CLAIM_TYPE'
]))
STATUS = dict((v, i) for i, v in enumerate(STATUS))
Ytrain = np.array([STATUS[v] for v in
ytrain_df['CLAIM_TYPE']])
Ytest = np.array([STATUS[v] for v in
ytest_df['CLAIM_TYPE']])
clf = SGDClassifier(n_jobs=4, loss='log')#,
class_weight='auto')#weights)
clf.fit(Xtrain, Ytrain)
pred = clf.predict(Xtest)
proba = clf.predict_proba(Xtest)
Binary Classifier Multiclass Classifier](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-101-2048.jpg)
![104
Search principles
• Most existing search systems are based off
indices
• Same as the indices found at the end of books
• Lucene is the most well-known library to
handle these
• Consider the query “the best search engines”
• First, break up the query in terms, remove
common ones, and normalize
• Yields [“best”, “search”, “engine”]
• Look up each term in the index dictionary
• Yields a list of documents per term (called an inverted
list)
• Find common documents in all list
• Sort the results in the desired order
(Image from https://stackoverflow.com/questions/17272050/book-index-page-layout-using-html5-and-css)](https://image.slidesharecdn.com/presentationalbi13112018compressed-181116081206/75/Datasciencein-E-commerce-industry-104-2048.jpg)
Uploaded byRakuten Group, Inc.
Datasciencein E-commerce industry
This document discusses recommendations and personalization techniques used at Rakuten. It describes the challenges of recommendations including different languages, user behaviors, and business areas. It provides an overview of recommendation systems and discusses approaches like collaborative filtering using user-user or item-item similarities, and matrix factorization. The document also discusses how to generate recommendations from unary data using co-occurrence analysis and similarity metrics.
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Similar to Datasciencein E-commerce industry
Datasciencein E-commerce industry
- 1. Datascience in E-commerceindustry Vincent MICHEL, Big Data EU, Rakuten.Inc vincent.michel@mail.rakuten.com
- 3. 3 Rakuten Group Worldwide https://global.rakuten.com/corp/about/index.html#strengths2018/11/05 Recommendation challenges • Different languages • Users behavior • Business areas
- 4. 4 Rakuten Group innumbers Rakuten in Japan • > 12.000 employees • > 48 billions euros of GMS • > 100.000.000 users • > 250.000.000 items • > 40.000 merchants https://global.rakuten.com/corp/ 2018/11/05 Rakuten Group • Kobo 18.000.000 users • Viki 28.000.000 users • Viber 345.000.000 users
- 5. 5 Rakuten Ecosystem Rakuten globalecosystem : • Member-based business model that connects Rakuten services • Rakuten ID common to various Rakuten services • Online shopping and services; • Main business areas: E-commerce, Internet finance, Digital content https://global.rakuten.com/corp/about/index.html#strengths 2018/11/05 https://global.rakuten.com/corp/about/history.html 2018/11/05 Recommendation challenges • Cross-services • Aggregated data • Complex users features
- 6. 6 Rakuten’s e-commerce: B2B2CBusiness Model Business to Business to Consumer: • Merchants located in different regions / online virtual shopping mall • Main profit sources • Fixed fees from merchants • Fees based on each transaction and other service Recommendation challenges • Many shops • Items references • Global catalog
- 7. 7 Big Data @Rakuten Mission: Development and operations of internal systems for: • Recommendations • Search • Targeting • User behavior tracking Average traffic: • > 100.000.000 events / day • > 40.000.000 items view / day • > 50.000.000 search / day • > 750.000 purchases / day Technology stack: • Java / Python / Ruby • Solr / Lucene • Cassandra / Couchbase • Hadoop / Hive / Pig • Redis / Kafka
- 8. 88 Short Bio ESPCI: engineerin Physics / Biology ENS Cachan: MVA Master Mathematics Vision and Learning INRIA Parietal team: PhD in Computer Science Understanding the visual cortex by using classification techniques Logilab – Development and data science consulting Data.bnf.fr (French National Library open-data platform) Brainomics (platform for heterogeneous medical data) Education Experience Rakuten PriceMinister– Senior Developer and data scientist Data engineer and data science consulting Rakuten – Recommendations & Personalization team lead Lead a team of engineers, datascientists and project managers
- 9. 99 Software engineering Lessons learnedfrom (painful) experiences
- 10. 10 Do not redoit yourself ! Lots of interesting open-source libraries for all your needs • Test first on a small POC, then contribute/develop • Scikit-learn, pandas, Caffe, Scikit-image, opencv, …. • Be careful: it is easy to do something wrong ! Open-data • More and more open-data for catalogs, … • E.g. data.bnf.fr: ~ 2.000.000 authors, ~ 200.000 works, ~ 200.000 topics Contribute to open-source • Unless you are doing some kind of super magical algorithm • Is there a need / pool of potential developers ? • Do it well (documentation / test) • May bring you help, bug fixes, and engineers ! But it takes time and energy
- 11. 11 Quality in datascience software engineering Never underestimates integration cost • Easy to write a 20 lines Python code doing some 883fancy Random Forests… • …that could be hard to deploy (data pipeline, packaging, monitoring) • Developer != DevOps != Sys admin Make it clean from the start (> 2 days of dev or > 100 lines of code) • Tests, tests, tests, tests, tests, tests, tests, … • Packaging / supervision / monitoring • Release often release earlier • Documentation, Agile development, Pull request, code versioning Choose the right tool • Do you really need this super fancy NoSQL database to store your transactions?
- 12. 12 Monitoring and alerting:building datascience product Hardware (CPU, IO, …) Software (Errors, requests, …) Datascience (KPIs, …)
- 13. 1313 Hiring remarks Selling yourselfas a (good) data scientist
- 14. 14 Defining yourself asa data scientist Do not try to sell yourself as a unicorn! Define your skills (and unicorns no longer exist…)
- 15. 15 Few remarks onhiring – my personal opinion Be careful of CVs with buzzwords! • E.g. “IT skills: SVM (linear, non-linear), Clustering (K-means, Hierarchical), Random Forests, Regularization (L1, L2, Elastic net…) …” • It is like as someone saying “ IT skills: Python (for loop, if/else pattern, …) Hungry for data? • Loving data is the most important thing to show • Opendata? Personal project? Curious about data? (Hackaton?) • Pluridisciplinary == knowing how to handle various datasets Improve your IT skills • Should be able to install/develop new libraries/algorithms • A huge part of the job could be to format / cleanup the data • Experience VS education -> Autonomy
- 16. 1616 Knowing the generalcontext Few remarks about GDPR
- 17. 17 What is theGDPR? Adopted in April 2016 and applicable as of May, 25th 2018 Replaces all the national legislations about the handling of personal data in Europe Until now, 1995 Directive which had been transposed differently among the EU countries In France, the law « Informatique et libertés » is going to be modified There still will be other differing sources: national case law and national data protection authorities (CNIL) doctrines Still pending: E-privacy Regulation adoption (about cookies and OTT)
- 18. 18 Why GDPR ? Whywas the GDPR passed? • Harmonisation of the European rules • To directly target non-European companies making business with EU data • To empower citizens and give them control over their data Why is the GDPR important? • Fines of up to 4% of the global annual turnover or 20 million euros. Now in France: max fine = 3 million euros • Loss of reputation and future customers • NGOs can bring claims on behalf of individuals • Burden of proof is on the company
- 19. 19 What will theGDPR really change? Accountability principle : less formalities to the DPA but more internal preparatory works (DPIA) and possibly higher fines in case of a control (on-site or online) Mandatory Data Protection Officer (records of processings) New obligations to data processors Security breach notification to the DPA (and even to the users in some cases) 72 hours max after a security incident New user right: data portability
- 20. 20 When is theGDPR applicable? The GDPR is applicable Yes No The GDPR is not applicable Does your business offer services to the EU? Do you provide your service in any European languages? Does your service use/accept any European currency? Are EU customers specifically addressed? (delivery) Profiling Tracking by cookies or otherwise Analysis of personal preferences / behavior Yes No Yes No Does your business collect, use or process personal data? But other privacy laws may apply Yes Is an office of your business in the EU? No Do you monitor individuals in the EU?
- 21.
- 22. 2222 Recommendations & Personalization TheBig Picture
- 23. 23 What are recommendations? https://www.rinapiccolo.com/piccolo-cartoons/ A recommender system seeks to predict the "rating" or "preference" a user would give to an item (wikipedia)
- 24. 24 Recommendations are generic Contextualfeatures Recommendations engine Input entities Items / Products Categories Users Shops Widgets/UI sections Output entities Items / Products Categories Users Shops Widgets/UI sections
- 25. 25 What is personalization? “Personalization,consists of tailoring a service or a product to accommodate specific individuals, sometimes tied to groups or segments of individuals” (wikipedia) https://www.rakuten.co.jp 2018/11/07
- 26. 26 Personalization usecases Left columnlinks Main widgets Top header links Push less but relevant content to the customer Push dynamic content to fit the context to the customer Push the most attractive content to the customer first A β δ α 1 B 2 B α β δ 2 A 1 A δ α 1 β 2 A 1 A β δ α 1 B 2 A δ α 1
- 27. 27 What are industrialcompanies doing? “Netflix member loses interest after perhaps 60 to 90 seconds of choosing” [source] “Netflix recommender system is used on most screens of the Netflix product beyond the homepage, and in total influences choice for about 80% of hours streamed at Netflix.” [source] “Already, 35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendations based on such algorithms.” [source]
- 28. 28 Recommendations: different usagesfor different contexts Best offers, Faster navigation, Serendipity, Complementary / substitute items… https://www.rakuten.fr 2018/11/05
- 29.
- 30. 30 Recommender System overview Datasources (BI,catalog…) Delivery API Users data Tracker Realtime context
- 31. 31 Challenges in Recommendations Itemscatalogues • Catalogue for multiple shops with different items references ? Items similarity / distances • Cross services aggregation ? • Lots of parameters ? Recommendations engine • Best / optimal recommendations logic ? Evaluation process • Offline / online evaluation ? • Long-tail ? KPI ? Items Catalogue Items Similarity Recommendations engine Evaluation Process
- 32. 32 Recommendations – Twoaxis strategy Recommendations candidates Ranked candidates Candidates retrieval Realtime ranking Batch logic 1 Batch logic 2 Batch logic 3 Batch logic 4 AXIS 1 Candidates generation strategy (Cocounts, Prod2vec, Word2vec, Top, Content-based) AXIS 2 Candidates ranking strategy / Learning-to-rank (User context, Item context, Page context, External context) ML / AI algorithm +Context & features e.g. history, item, time, …
- 33. 33 Recommendation datatypes Ratings Numerical feedbacksfrom the users Sources: Stars, reviews, … ✔ Qualitative and valuable data ✖ Hard to obtain Scaling and normalization ! Unitary data Only 0/1 without any quality feedback Sources: Click, purchase… ✔ Easy to obtain (e.g. tracker) ✖ No direct rating Users Items 1 3 2 5 2 2 4 1 3 1 5 4 4 1 3 Users Items 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
- 34. 34 Items Catalogues Use differentlevels of aggregation to improve recommendations Category-level (e.g. food, soda, clothes, …) Product-level (manufactured items) Item in shop-level (specific product sell by a specific shop) Increased statistical power in co-events computation Easier business handling (picking the good item)
- 35. 3535 Recommendations & Personalization Howto do work with unitary data?
- 36. 36 Cocounts for binary/ Unitary data Only occurences of items views/purchases/… Jaccard distance Cosine similarity Conditional probability
- 37. 37 Co-occurrences and SimilaritiesComputation Multiple possible parameters: • Size of time window to be considered: Does browsing and purchase data reflect similar behavior ? • Threshold on co-occurrences Is one co-occurrence significant enough to be used ? Two ? Three ? • Symmetric or asymmetric Is the order important in the co-occurrence ? A then B == B then A ? • Similarity metrics Which similarity metrics to be used based on the co-occurrences ? Only access to unitary data (purchase / browsing) Use co-occurrences for computing items similarity
- 38. 38 Co-occurrences Example Browsing Purchase Session ?Session ?Time window 1 Session ?Time window 2 07/11/2015 08/11/2015 08/11/2015 24/11/2015 08/11/2015 08/11/2015 10/09/2015 08/09/2015 10/09/2015
- 39. 39 Co-occurrences Example Co-purchases Co-browsing Classical co-occurrences Complementary items Substitute items Otherpossible co-occurrences Items browsed and bought together Items browsed and not bought together “You may also want…” “Similar items…” 08/11/2015 08/11/2015 08/11/2015 07/11/2015 08/11/201510/09/2015 08/09/2015 07/11/2015
- 40. 4040 Recommendations & Personalization Howto do it for ratings data?
- 41. 41 Algorithm 1 -Collaborative filtering User-user #items < #users Items are changing quickly Item-item #items >> #users Users Items 1 3 2 5 2 2 4 1 3 1 5 4 4 1 3 ? 1 – Compute users similarities (cosine-similarity, Pearson) 2 – Weighted average of ratings
- 42. 42 Algorithm 2 -Matrix factorization Users Items 1 3 2 5 2 2 4 1 3 1 5 4 4 1 3 -0.7 1 0.4 … … … … … 2.3 0.2 -0.3 Items 0.5 0.3 … 1.2 … 1.2 -0.2 … -3.2 Users ~ X • Choose a number of latent variables to decompose the data • Predict new rating using the product of latent vectors • Use gradient descent technics (e.g. SGD) • Add some regularization
- 43. 43 Matrix factorization –MovieLens example Read files import csv movies_fname = '/path/ml-latest/movies.csv' with open(movies_fname) as fobj: movies = dict((r[0], r[1]) for r in csv.reader(fobj)) ratings_fname = ’/path/ml-latest/ratings.csv' with open(ratings_fname) as fobj: header = fobj.next() ratings = [(r[0], movies[r[1]], float(r[2])) for r in csv.reader(fobj)] Build sparse matrix import scipy.sparse as sp user_idx, item_idx = {}, {} data, rows, cols = [], [], [] for u, i, s in ratings: rows.append(user_idx.setdefault(u, len(user_idx))) cols.append(item_idx.setdefault(i, len(item_idx))) data.append(s) ratings = sp.csr_matrix((data, (rows, cols))) reverse_item_idx = dict((v, k) for k, v in item_idx.iteritems()) reverse_user_idx = dict((v, k) for k, v in user_idx.iteritems())
- 44. 44 Matrix factorization –MovieLens example Fit Non-negative Matrix Factorization from sklearn.decomposition import NMF nmf = NMF(n_components=50) user_mat = nmf.fit_transform(ratings) item_mat = nmf.components_ Plot results component_ind = 3 component = [(reverse_item_idx[i], s) for i, s in enumerate(item_mat[component_ind , :]) if s>0.] For movie, score in sorted(component, key=lambda x: x[1], reverse=True)[:10]: print movie, round(score) Terminator 2: Judgment Day (1991) 24.0 Terminator, The (1984) 23.0 Die Hard (198 19.0 Aliens (1986) 17.0 Alien (1979) 16.0 Exorcist, The (1973) 8.0 Halloween (197 7.0 Nightmare on Elm Street, A (1984) 7.0 Shining, The (1980) 7.0 Carrie (1976) 7.0 Star Trek II: The Wrath of Khan (1982) 10.0 Star Trek: First Contact (1996) 10.0 Star Trek IV: The Voyage Home (1986) 9.0 Contact (1997) 8.0 Star Trek VI: The Undiscovered Country (1991) 8.0 Blade Runner (1982) 8.0
- 45. 4545 Recommendations & Personalization Howto do content-based ?
- 46. 46 Content-based: what shouldwe use ? Attribute-based Content-based • Encoded features (e.g. one-hot- encoding) • Represent documents in the features space • Find similar documents (Knn, Kd-tree, …) • Encoded textual content of documents • Represent textual content in an embeddings space • Find similar documents (Knn, Kd-tree, …) Can be linked to Search Engine https://www.rakuten.fr 2018/11/05
- 47. 47 Example of feature:Named entities in product description Sample of code with Polyglot from polyglot.text import Text text = Text(blob) for sent in text.sentences: print(sent, "n") for entity in sent.entities: print(entity.tag, entity) (Sentence("A New York, au printemps 2008, alors que l'Amérique bruisse des prémices de l'élection présidentielle, Marcus Goldman, jeune écrivain à succès, est dans..."), 'n') (u'I-LOC', I-LOC([u'New', u'York'])) (u'I-PER', I-PER([u'Marcus', u'Goldman'])) (Sentence("Lire la suite la tourmente : il est incapable d'écrire le nouveau roman qu'il doit remettre à son éditeur d'ici quelques mois."), 'n') (Sentence("Le délai est près d'expirer quand soudain tout bascule pour lui : son ami et ancien professeur d'université, Harry Quebert, l'un des écrivains les plus respectés du pays, est rattrapé par son passé et se retrouve accusé d'avoir assassiné, en 1975, Nola Kellergan, une jeune fille de 15 ans, avec qui il aurait eu une liaison."), 'n') (u'I-PER', I-PER([u'Harry', u'Quebert'])) (u'I-PER', I-PER([u'Nola', u'Kellergan'])) https://fr.shopping.rakuten.com/mfp/3011174/la-verite-sur-l-affaire-harry-quebert-joel-dicker-livre?pid=171972011 2018/11/05
- 48. 48 Word2vec: two-layer neuralnetwork Distributed representation of words: • Continuous bag-of-words: predict current word from surrounding words only • Skip-gram: use current word to predict surrounding words https://skymind.ai/wiki/word2vec
- 49.
- 50. 50 Word2vec: Code sample importstring import unidecode import requests from bs4 import BeautifulSoup refs = ["annee1", "artgrdp1", "ballades1", "bugjarg1", "contemplA2", "contemplB2”, "feuilles1", "hugoshak1", "legend1", "legendet21", "nddp1", "oriental1”, "quatrevt1", "rayons1", "ruesboi1", "satan1"] textes = "" for ref in refs: print ref res = requests.get("http://abu.cnam.fr/cgi-bin/donner_html?%s" % ref) text = BeautifulSoup(res.content, 'lxml').text textes += text.split("DEBUT DU FICHIER")[1].split("FIN DU FICHIER")[0] sentences = [t for t in textes.replace("n", " ").replace("r", " ").split(".") if len(t) > 20] sentences = [t.lower().strip() for sentence in sentences for t in sentence.split(";")] words = [[t.strip().strip(string.punctuation) for t in sentence.split()] for sentence in sentences] words = [[unidecode.unidecode(t) for t in word] for word in words] Model learning with Gensim import gensim model = gensim.models.Word2Vec(words, min_count=5, size=500, sg=1)
- 51. 51 Word2vec: two-layer neuralnetwork We can use + and – operations on embedding vectors to find logical relationships model.most_similar("femme”) [('fille', 0.8448578715324402), ('danseuse', 0.8413887023925781), ('heureuse', 0.8404737114906311), ('fee', 0.8399657011032104), ('jolie', 0.8360834717750549), ('epouse', 0.8242064714431763), ('malheureuse', 0.823990523815155), ('jeune', 0.8126694560050964), ('creature', 0.8117369413375854), ('bohemienne', 0.8117284178733826)] model.most_similar(positive=["quasimodo", "femme"], negative=["homme"]) [('bohemienne', 0.7697243094444275), ("l'egyptienne", 0.7591158747673035), ('chevre', 0.7411096096038818), ('recluse', 0.7373903393745422), ('esmeralda', 0.733445405960083), ('rene-jean', 0.7310534715652466), ('parole', 0.7230771780014038), ('tourna', 0.715079665184021), ('vivement', 0.7149174213409424), ('condamnee', 0.7142783403396606)] model.most_similar("philosophe“) [('ecolier', 0.9446097016334534), ('soulier', 0.9436287879943848), ('bandit', 0.9423332810401917), ('bonhomme', 0.9420523643493652), ('ennemi', 0.938879132270813), ('gentilhomme', 0.9362510442733765), ('savant', 0.9335594177246094), ('officier', 0.9284510612487793), ('traitre', 0.9282448887825012), ('damne', 0.926963210105896)]
- 52. 52 Convolutional neural network(CNN) https://www.mathworks.com/solutions/deep-learning/convolutional-neural-network.html • Feature-sharing • Not hand-designed • It boosted algorithms performances in many tasks! • Data driven (but you need data!)
- 53. 5353 Recommendations & Personalization Deep-learningfor E-commerce ?
- 54. 54 Prod2vec: purchases sessionas a sentence E-commerce in Your Inbox: Product Recommendations at Scale, Grbovic et al. ≈ Purchase 08/11/2015 24/11/201508/11/201508/09/2015 10/09/2015 This is not Romeo, he's some other where.I am not here;Tut, I have lost myself; Apply Word2Vec on a sentence of “purchases”
- 55. 55 Prod2vec: Theory Prod2vec learnsa low-level embedding representation of products using the skip-gram model E-commerce in Your Inbox: Product Recommendations at Scale, Grbovic et al. Objective function (S is the set of sessions) Probability of seeing the neighboring product pi+j given product pi v and v’ are the input and output vector representation (that should be learned). Similar products should be closed in the vector space.
- 56. 56 Prod2vec: embedding ofproducts with similar neighborhood Purchase Purchase
- 57. 57 Prod2vec: Python examplewith Gensim Model learning with Gensim from gensim.models import Word2Vec fobj = open(’purchases.json') model = Word2Vec(fobj, size=100, window=5, min_count=1, workers=4) embeddings = model.wv.syn0 indices = model.wv.index2word KNN computation with Falconn import falconn params_cp = falconn.get_default_parameters(embeddings.shape[0], embeddings.shape[1]) params_cp.lsh_family = falconn.LSHFamily.CrossPolytope params_cp.distance_function = falconn.DistanceFunction.EuclideanSquared lsh = falconn.LSHIndex(params_cp) lsh.setup(embeddings) query = lsh.construct_query_object() for i in range(embeddings.shape[0]): res = query.find_k_nearest_neighbors(embeddings[i], knn)
- 58. 5858 Recommendations & Personalization Learningto Rank How to adapt to each specific context?
- 59. 59 LTR in aNutshell Re-orders the recommendations based on the features Store features Offline Machine Learning Store model Real time ML Recommend API delivery https://static.googleusercontent.com/media/research.google.com/ru//pubs/archive/45530.pdf
- 60. 60 LTR features About theuser About the item External factors
- 61. 61 Problem setting ts itemritem 1 A B .1 .3 .2 .9 .4 .0 .2 0 1 A C .9 .7 .6 .0 .0 .3 .6 0 1 A D .4 .8 .6 .3 .2 .1 .0 1 2 B A .3 .1 .5 .7 .1 .9 .1 0 2 B E .1 .5 .2 .3 .2 .8 .7 1 Learn any model (linear, non-linear, …) click
- 62. 62 Learning to Rankapproaches • Pointwise approach: Consider each pair (document, target) separately (clicked, purchased). • Pairwise approach: Consider order of two documents and minimize inversions errors • Listwise approach: Consider all documents, and try to optimize the overall/average score 1 Not Clicked 2 Clicked 3 Not Clicked 4 Clicked 5 Clicked 6 Not Clicked https://www.rakuten.fr 2018/11/05
- 63. 63 Learning to Rankapproaches algorithm 1 Not Clicked 2 Clicked • Pointwise approach == (feature11, feature21, …, 0) == (feature12, feature22, …, 1) 3 Not Clicked == (feature13, feature23, …, 0) 1 Not Clicked• Pairwise approach == (feature11, feature21, feature12, feature22…, 0)2 Clicked == (feature11, feature21, feature13, feature23…, 1)1 Not Clicked 3 Not Clicked == (feature12, feature22, feature13, feature23…, 2)3 Not Clicked 2 Clicked 1 Not Clicked• Listwise approach == (feature11, feature21, feature12, feature22…, (2, 1, 3))2 Clicked 3 Not Clicked Regression problem predict document score Classification problem which document is better More complex problem… optimize the value of one of the evaluation measures
- 64. 64 Learning to Rankapproaches solutions Regression/classification problem: • SVM • SGD • Random Forests Listwise measures: • nDCG: normalized Discounted Cumulative Gain Doc 1 Rel = 5 Doc 2 Rel = 3 Doc 3 Rel = 1 Doc 4 Rel = 4 Doc 5 Rel = 2 CG5 = 15; DCG5 = 9.89; IDCG5 = 10.27, nDCG5 = 0.96 Doc 1 Rel = 5 Doc 2 Rel = 3 Doc 3 Rel = 4 Doc 4 Rel = 1 Doc 5 Rel = 2 CG5 = 15; DCG5 = 10.1; IDCG5 = 10.27, nDCG5 = 0.98
- 65. 6565 Recommendations & Personalization RecommendationsQuality How to evaluate recommendations ?
- 66. 66 Recommendation Quality Challenges Recommendationscategories • Cold start issue • External data ? • Cross-services ? • Hot products (A) • Top-N items ? • Short tail (B) • Long tail (C + D) Minor Product Major Product (Popular) New Product Old Product (A) (B) (D) (C)
- 67. 67 Offline Evaluation Pros/Cons • Convenientway to try new ideas • Fast and cheap • But hard to align with online KPI Approaches • Rescoring • Prediction game • Business simulator
- 68. 68 Public Initiative –Viki Recommendation Challenge http://www.dextra.sg/challenges/rakuten-viki-video-challenge 567 submissions from 132 participants
- 69. 69 A/B Testing Track users’interaction with the AB-test variants Compute statistical tests Choose which version to put in production A B control variation
- 70. 70 A/B Statistical test Donot peek A/B tests or stopped them before the end Abtest A Abtest B Abtest C Sample size should be fixed in advance 500 samples 1000 samples 1500 samples Stopped because Not significant Stopped because Not significant Kept because significant ✗ Abtest A Abtest B Abtest C 500 samples 1000 samples 1500 samples Kept because significant ✓ Kept because significant
- 71. 71 A/B Statistical test https://www.evanmiller.org/how-not-to-run-an-ab-test.html Whatsample size to use? Sample variance you expect the minimum effect you wish to detect https://www.evanmiller.org/ab-testing/sample-size.html
- 72. 72 Continuous A/B testprocess Short ABtest 2 or 3 days L1 (control): CVRia 0.21 L2: CVRia 0.03 L3: CVRia 0.7 Short ABtest 2 or 3 days L3 (control): CVRia 0.065 L1 (old control): CVRia 0.03 L4: CVRia 0.09 Short ABtest 2 or 3 days L4 (control): CVRia 0.07 L3 (old control): CVRia 0.72 L2: CVRia 0.03 Expected output: step-by-step increase of CVRia / orders
- 73. 73 Multi-arm bandit Testing differentmodels without knowing their outcome - Exploring the different models to estimate their rewards - Exploit the best model know so far It costs to test hypotheses ( == explore) Model A Model B Model C Model A Reward 1.1 Model B Model C Model A Reward 0.5 Model B Model C Model A Reward 0.8 Model B Model C Model A Reward 0.2 Model B Model C Model A Reward 0.9 Model B Model C Model A Reward 1.8 Model B Model C
- 74. 74 Epsilon-greedy strategy Many otherstrategies (e.g. see Wikipedia)… • Epsilon-first strategy: A pure exploration phase is followed by a pure exploitation phase. • Epsilon-decreasing strategy: The value of epsilon decreases over time. …and different bandits: • Contextual bandit: at each iteration, we have access to a vector of contextual features. • Constrained contextual bandit: a total budget is associated with the bandit. Models Other models Best model Model 1 Model 2 Model n 1/n 1/n 1/n ε 1-ε
- 75. 7575 Cleaning and improvingdatasets Record-linkage
- 76. 76 What is RecordLinkage? Record linkage (RL) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases) Wikipedia Usage for Recommendations • Global catalog • Items aggregation • Helps with cold start issues • Improved navigation Marketplace 1 Marketplace 2Reference dataset
- 77. 77 Linked Open Data LinkingOpen Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/"
- 78. 78 Semantic-web and RDFformat Triples: <subject> <relation> <object> URI: unique identifier http://dbpedia.org/page/Terminator_2:_Judgment_Day
- 79. 79 Record linkage forglobal recommendations • Linking products together in a service • Feature Engineering - Generate hierarchies of products • ✔ Improve statistical power of co-events computation by aggregation • ✔ If based on text, may be used for content-based recommendations • Based on Record Linkage technics (e.g. MinHashing) • Linking products together between services • Having a unique product id across services • Use recommendations from one service, for another new service • ✔ Avoid cold start issue • Cross-services recommendations • ✔ Show items from a service on another service pages • Linking products to an external database (Wikidata) • More info for item enrichment, UI, content-based recommendations
- 80. 80 Record linkage –The big picture Dataset 1 e.g. title, categories, price Blocking e.g. minhashing on the titles Dataset 2 e.g. title, categories, price Subset Dataset 1 Subset Dataset 2 Block 1 Subset Dataset 1 Subset Dataset 2 Block 2 Subset Dataset 1 Subset Dataset 2 Block n Subset Dataset 1 Subset Dataset 2X Comparisons based on attributes with specific distances For each block Links creation Data 1_1 == Data 2_n Data 1_2 == Data 2_p Data 1_3 == Data 2_1 …. Comparisons (distances computation)
- 81. 81 Record linkage –Naive approach Match items from one dataset to the other using distances Levenshtein Jaccard Python difflib Based on an algorithm published in the late 1980’s by Ratcliff and Obershelp under the hyperbolic name “gestalt pattern matching.” (see doc)
- 82. 82 Shingles and documentsrepresentation Shingles ~ Word-n-grams Split documents in a list of words groups A New York, au printemps 2008, alors que l'Amérique bruisse des prémices de l'élection présidentielle, Marcus Goldman, jeune écrivain à succès, est dans… E.g. word-3-grams • A New York, au • New York au printemps • … • à succès, est • succès, est dans Use jaccard distance between sets of shingles But…
- 83. 83 Record linkage –Problematic Combinatorial explosion 10^6 items X 10^6 items = 10^12 comparisons • Use blocking: divide and conquer approaches: n-gram indexes, clustering, minhashing… • Apply more computationally expensive approaches on each block
- 84. 84 Minhashing - Theory Howto compare shingles of different sizes? MINHASHING ! 1. Compute hash of shingles 2. Keep minimum hash value 3. Repeat for 200 different hashs We now have a 200-dimensions representation of all shingles Why is it working ? If 2 documents share the same minimum hash for two shingles -> They share that shingle. Document 1 Document 2 Shingle 1 Shingle 2 … Shingle 1 Shingle 2 … Hash Value1, Value2, … Value1’, Value2’, … Find minimum hash value
- 85. 85 Minhashing - Theory Wejust have to keep the minimum hash value for each document Huge computational and storage boost ! Randomly picking 200 shingles and comparing them between 2 documents ≈ Storing and comparing minimum values for 200 differents hash functions Jaccard(doc1, doc2) -> #Minhash(S1)==Minhash(S2) / nbhash But we still have to compare all documents together (even if the comparison is way faster)
- 86. 86 Local Sensitivity Hashing Whatis a Locality sensitive hashing (LSH)? It is a hash such that similar vectors tend to get similar hash values It generates ‘band’ of documents, where documents within a band are more or less similar, and should be compared with Minhashing Document 1 Document 2 Shingle 1 Shingle 2 … Shingle 1 Shingle 2 … Shingle 1 Shingle 2 … Shingle 1 Shingle 2 … Document 1 Document 2 Minhash 1 … Minhash 200 Minhash 1 … Minhash 200 Minhash 1 … Minhash 200 Minhash 1 … Minhash 200 Document 1 Document 4 Document 3 Document 5 … Minhashing comparison Minhashing comparison Minhashing comparison Shingle Minhashing Local Sensitivity Hashing
- 87. 87 Minhashing + LSH- Results Marketplace 1 Marketplace 2 https://www.wikidata.org/wiki/Q170564 bg Терминатор 2: Денят на страшния съд el Εξολοθρευτής 2: Μέρα Κρίσης en Terminator 2: Judgment Day es Terminator 2: el juicio final fr Terminator 2 : Le Jugement dernier ja ターミネーター2 ka ტერმინატორი 2: განკითხვის დღე Director: James Cameron Cast member: Arnold Schwarzenegger Cast member: Edward Furlong Follows: The Terminator Genre: action film Main subject: time travel, android Narrative location: Los Angeles https://item.rakuten.co.jp/auc-tecc/10016937/ 2018/11/07https://fr.shopping.rakuten.com/mfp/5705594/terminator-2-2 2018/11/07
- 88. 8888 Rakuten.fr datachallenge 2017 Usinguser reviews https://challengedata.ens.fr/fr/challenge/26/prediction_de_linteret_des_avis_utilisate urs.html
- 89. 89 Users reviews onRakuten.fr https://fr.shopping.rakuten.com 2016/10/10
- 90. 90 Challenge – Task1 Predict if a review is useful for other users or not May be use to boost interesting reviews on the website Classification (#useful / #total > 0.5) or Regression task on textual features https://fr.shopping.rakuten.com 2016/10/10
- 91. 91 Challenge – Task2 Predict the user stars number based on his/her review May be use to detect fraud and help improving the quality on the website Regression task (6 discrete values) on textual features https://fr.shopping.rakuten.com 2016/10/10
- 92. 92 Data samples product: b57c06ed94773c4d08bcefcdf8cbedd846bbdcba8d669a15d511b9acb92efeb43 review_title:“why not !” review_content: “Yess! est un jeu de communication réussi, intuitif, malin assez rapide avec peu de temps mort. Voilà un jeu au rapport plaisir/prix qui est bien placé.” review_note: 4 feedback_positive_count: 0 feedback_negative_count: 0 product: 12d1407836441fc39805916ecb705604bcb539bd70b477c82d57f5043977102 review_title: “Lave linge parfait” review_content: “Seul point négatif : la mise en réseau de la machine...Mais ce n'est pas ce que l'on recherche le plus dans un lave linge” review_note: 4 feedback_positive_count: 0 feedback_negative_count: 0
- 93. 93 Expected results The expectedresults are probabilities of being useful (Class 1) Proba: 0.99 Je vien d'accerir ses enceintes d'une qualité de son incroyable . Des basses profondes et puissantes . Pour amoureux de son ! Proba: 0.0611228262615 Parfait PARFAIT PARFAIT PARFAIT PARFAIT PARFAIT PARFAIT PARFAIT PARFAIT PARFAIT PARFAIT
- 94. 94 Baseline code example importpandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn import linear_model from sklearn.cross_validation import KFold STOPWORDS = set(['alors', 'au', 'aucuns', 'aussi', 'autre', 'avant', 'avec’ …]) fname = '/path/to/reviews_test_clean 2.csv’ df = pd.read_csv(fname) df['feedback_ratio'] = df['feedback_positive_count'].astype(float) / (df['feedback_positive_count’] + df['feedback_negative_count']) df['feedback_class'] = df['feedback_ratio'] >= 0.5 X = df['review_content'].values Y = df['feedback_class'].to_dense().values kf = KFold(n=len(X), n_folds=5) all_pred , all_test = [], [] for train, test in kf: Xtrain, Xtest = X[train], X[test] Ytrain, Ytest = Y[train], Y[test] vect = TfidfVectorizer(stop_words=STOPWORDS) Xtrain = vect.fit_transform(Xtrain) Xtest = vect.transform(Xtest) clf = linear_model.SGDClassifier() clf.fit(Xtrain, Ytrain) pred = clf.predict(Xtest) all_pred.extend(pred) all_test.extend(Ytest)
- 95. 9595 Rakuten.fr datachallenge 2018 Predictionof transaction claims status https://challengedata.ens.fr/en/challenge/39/prediction_of_transaction_claims_status. html
- 96. 96 Claims predictions inE-commerce Claims have a huge impact in terms of user experience + cost Claims can be dealt with differently following the different cases (broken, fake, …) Predict if a transaction has a probability to lead to a claim -> focus on risky transactions. Possibly a huge impact in the whole E-commerce field!
- 97. 97 Dataset • ID: identifierof the sample • SHIPPING_MODE: mode of shipping of the product • (RECOMMANDE, NORMAL, …) • SHIPPING_PRICE: cost of shipping, if existing • (<1, 1<5, 5<10, 10<20, >20) • WARRANTIES_FLG: True if a warranty has been taken by the buyer • WARRANTIES_PRICE: Price of warranty, if existing • (<5, 5<20, 20<50, 50<100, 100<500, >500) • CARD_PAYEMENT: transactions paid by card • COUPON_PAYEMENT: transactions paid with a discount coupon • RSP_PAYEMENT: transactions paid with Rakuten Super Points • WALLET_PAYMENT: transactions paid with PriceMinister-Rakuten wallet • PRICECLUB_STATUS: status of the buyer • (UNSUBSCRIBED, PLATINUM, …) • REGISTRATION_DATE: year of registration of the buyer • PURCHASE_COUNT: binarisation of buyer's previous purchases count • (<5, 5<20, 20<50, 50<100, 100<500, >500) • BUYER_BIRTHDAY_DATE: year of birth of the buyer • BUYER_DEPARTMENT: department of the buyer or -1 • BUYING_DATE: year and month of the purchase • SELLER_SCORE_COUNT: binarisation of the seller's previous sales count • (<100, 100<103, 103<104, 104<105, 105<106, >106) • SELLER_SCORE_AVERAGE: score of the seller on PriceMinister-Rakuten • SELLER_COUNTRY: country of the seller • (FRANCE METROPOLITAN, CHINA, …) • SELLER_DEPARTMENT: department of the seller or -1 • PRODUCT_TYPE: type of the purchased product • (TOYS, CELLPHONE_ACCESSORY, …) • PRODUCT_FAMILY: family of the purchased product • (ELECTRONICS, BABY, …) • ITEM_PRICE: binarisation of the purchased product • (<10, 10<20, 20<50, 50<100, 100<500, 500<1000, 1000<5000, >5000)
- 98. 98 Challenges Complex interactions thatinvolves multiple factors (probably not all contained in the features) and subjective information (the same shop does not always send broken products…) Unbalanced classes! Categorical features + numerical features (beware of ranges!) Find some socio-demographics/behavioral features (e.g. based on country)
- 99. 99 Baseline Metric: AUC weightedmetric (from sklearn) Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score Algorithm used for benchmarks (naive and classic!) • Random forests classifier (from sklearn), with 200 estimators • Classical preprocessors (from sklearn): OneHotEncoder, LabelEncoder Result obtained: 0.574 AUC weighted metric.
- 100. 100 Baseline code example fromcollections import defaultdict import pandas as pd import numpy as np import sklearn.preprocessing from scipy.sparse import hstack xtrain_df = pd.read_csv('training_X.tsv’, delimiter='t’) xtest_df = pd.read_csv('test_X.tsv’, delimiter='t’) ytrain_df = pd.read_csv('training_Y.tsv’, delimiter='t’) ytest_df = pd.read_csv('test_Y.tsv’, delimiter='t’) CATS = ['WARRANTIES_FLG’, `'SHIPPING_MODE`, …] LABELS = defaultdict(set) for cat in CATS: d = set(xtrain_df[cat].unique()).union(set(xtest_df[cat].un ique())) LABELS[cat] = dict((v, i) for i, v in enumerate(d)) NUMERICAL = ['CARD_PAYMENT’, COUPON_PAYMENT’,…] ENCODERS = dict() COLUMNS = [] Xtrain = create_matrix(xtrain_df, COLUMNS) Xtest = create_matrix(xtest_df) def create_matrix(df, COLUMNS=None): print df.shape X = df[NUMERICAL].to_sparse() if COLUMNS is not None: COLUMNS.extend(NUMERICAL) for cat in CATS: if COLUMNS is not None: for v in sorted(LABELS[cat].iteritems(), key=lambda x: x[1]): COLUMNS.append('%s (%s)' % (cat, v[0])) data = df[cat] data = np.ravel([LABELS[cat][v] for v in data]) data = np.reshape(data, [data.size, 1]) if cat in ENCODERS: data = ENCODERS[cat].transform(data) else: oenc = sklearn.preprocessing.OneHotEncoder() data = oenc.fit_transform(data) ENCODERS[cat] = oenc X = hstack((X, data)) return X
- 101. 101 Baseline code example lenc= sklearn.preprocessing.LabelEncoder() Ytrain = lenc.fit_transform(ytrain_df['CLAIM_TYPE']) Ytrain[Ytrain != 0] = 1 lenc = sklearn.preprocessing.LabelEncoder() Ytest = lenc.fit_transform(ytest_df['CLAIM_TYPE']) Ytest[Ytest != 0] = 1 from sklearn.ensemble import RandomForestClassifier clf = sklearn.ensemble.RandomForestClassifier(n_estimator s=50) clf.fit(Xtrain.toarray(), Ytrain) pred = clf.predict(Xtest.toarray()) ytrain_df['CLAIM_TYPE'][ytrain_df['CLAIM_TYPE'] == '-'] = 'NO COMPLAIN' ytest_df['CLAIM_TYPE'][ytest_df['CLAIM_TYPE'] == '-'] = 'NO COMPLAIN’ STATUS = set(ytrain_df['CLAIM_TYPE']).union(set(ytest_df['CLAIM_TYPE' ])) STATUS = dict((v, i) for i, v in enumerate(STATUS)) Ytrain = np.array([STATUS[v] for v in ytrain_df['CLAIM_TYPE']]) Ytest = np.array([STATUS[v] for v in ytest_df['CLAIM_TYPE']]) clf = SGDClassifier(n_jobs=4, loss='log')#, class_weight='auto')#weights) clf.fit(Xtrain, Ytrain) pred = clf.predict(Xtest) proba = clf.predict_proba(Xtest) Binary Classifier Multiclass Classifier
- 102. 102102 Big data atscale Search engine
- 103. 103 Search principles The goalof search is to help users efficiently find the most relevant documents for a given query. • Documents • Depend on how the data is modeled • Marketplace: product, offer (product sold by a merchant), SKU (variation of a product), … • Video streaming: movie, tv series, tv episode, … • Query • Terms: what goes in the search box • Filters: navigation items • Relevancy • Based off the data: by price, by freshness, … • Based off user behavior: clicks, purchases, … • Based off text semantics: entity extraction, … • Based off corpus statistics: terms frequencies, … • Efficiency • Low response time • Assistance: spellcheck, autocomplete, …
- 104. 104 Search principles • Mostexisting search systems are based off indices • Same as the indices found at the end of books • Lucene is the most well-known library to handle these • Consider the query “the best search engines” • First, break up the query in terms, remove common ones, and normalize • Yields [“best”, “search”, “engine”] • Look up each term in the index dictionary • Yields a list of documents per term (called an inverted list) • Find common documents in all list • Sort the results in the desired order (Image from https://stackoverflow.com/questions/17272050/book-index-page-layout-using-html5-and-css)
- 105. 105 Search, big dataand e-commerce • Indexing challenges • Volume of data: large marketplaces contain a lot of documents • Update rate: information can change fast; price, inventory • Search challenges • Query rate: lots of end-users querying at the same time • Features: not only document retrieval, but also navigation, statistics, … • Relevancy challenges • Document do not usually contain natural language • More susceptible to spam and merchants trying to game the system • Need to balance well-selling items with discovery, especially for newer releases of popular products • Multi-language support for global market places • Operation challenges • Very large scale systems (2000+ nodes) need robust deployment and monitoring • Resources distribution (models, linguistic resources)
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- 107. 107107 Datascience everywhere ! Rakutenprovides marketplaces worldwide Specific challenges for recommendations Items catalogue: reinforce statistical power of co-occurrences across shops and services; Items similarities: find the good parameters for the different use-cases; Recommendations models: what is the best models for in-shop, all-shops, personalization? Evaluation: handling long-tail? Comparing different models?
- 108. 108108 We are Hiring! Positions http://www.priceminister.com/recrutement/?p=197 DataScientist / Software Developer • Build algorithms for recommendations, search, targeting • Predictive modeling, machine learning, natural language processing • Working close to business • Python, Java, Hadoop, Couchbase, Cassandra… Also hiring: search engine developers, big data system administrators, etc.
- 109. 109109 Thanks ! Questions ? Moreon Rakuten tech initiatives http://www.slideshare.net/rakutentech http://rit.rakuten.co.jp/oss.html http://rit.rakuten.co.jp/opendata.html Positions http://www.priceminister.com/recrutement/?p=197