Sitecore: Session recommendation engine

#SitecoreSYM#SitecoreSYM © 2001-2018 Sitecore Corporation A/S. All rights reserved. Sitecore® and Own the Experience® are registered trademarks
of Sitecore Corporation A/S. All other brand and product names are the property of their respective owners.
1
Symposium Session
Recommendation
Engine #mysym
Varun Nehra
Dmytro Shevchenko

#SitecoreSYM 2
• SitecoreTechnology MVP
(2016–2018)
• 7+ years of Sitecore experience
• 10+ Sitecore deliveries/15+ client
engagements
• Live/work in Germany
Introduction
© 2001-2018 Sitecore Corporation A/S. All rights reserved. Sitecore® and Own the Experience® are registered trademarks
DMYTRO SHEVCHENKO
Sitecore Solutions Architect @ EPAM
dmytro_shevchenko_de@epam.com
@shedal
www.structuredcode.com
VARUN NEHRA
Sr. Sitecore Solutions Architect @ EPAM
• 7+ years of Sitecore experience
• 13+ Sitecore deliveries/30+
client engagements
• Live/work in Atlanta, GA
varun_nehra@epam.com
@sitecorevarun
www.sitecorevarun.com

#SitecoreSYM 3
EPAM’s Sitecore Practice
100+ Projects 50+ Customers
12
SITECORE
MVPS
7
SITECORE SITE OF
THE YEAR AWARDS
50+
CERTIFIED SITECORE
DEVELOPERS
450+
EXPERIENCED
PROFESSIONALS
PLATINUM
PARTNER WITH
10+
YEARS OF
EXPERIENCE
DELIVERED FOR

#SitecoreSYM
Agenda
Machine Learning
1. Overview
2. How it works
3. Demo
4
Sitecore xConnect
1. Overview
2. How it works
3. Demo
What’s Next with Sitecore Cortex
Q&A

#SitecoreSYM
UserStory
AS A Sitecore Symposium attendee, I WOULD LIKETO get
recommendations for sessions I should attend, SOTHAT I don’t
need to review every breakout session and do a coin toss to resolve
conflicting sessions in my agenda.
5

#SitecoreSYM 6
PreparingThe Solution
ONE AMAZING
EVENT
VIBRANT COMMUNITY
OF DEVELOPERS &
MARKETERS
MULTI-CHANNEL
CONTENT
EXPERIENCE
PLATFORM
BYOML (BRING
YOUR OWN ML)+ + + +
Recipe
MULTI-CHANNEL
CONTENT
MACHINE
LEARNING
PERSONALIZED
AGENDA
#mysym
SITECORE
XP
+ + =
Key Ingredients

#SitecoreSYM 7
Sitecore Experience Platform 9
One platform to rule them all, one
event to find them, many channels to
bring them all and in the ML bind them.

#SitecoreSYM 8
Our Solution
Sitecore XP 9 Experience management & personalization
Sitecore xConnect Creates contact profile with social media interactions
Azure ML Supervised machine learning
SymposiumTracks &Topics Labeled dataset for model training in supervised learning
Symposium Session Content Personalized agenda for attendees
I N G R E D I E N T W H AT I S D O E S

#SitecoreSYM 9
Symposium Session Recommendation Engine

#SitecoreSYM
Demo – Session Recommendation Engine
10

#SitecoreSYM
How ItWorks – Machine Learning
• Supervised Learning
• Azure MLTemplate
11

#SitecoreSYM 12
How ItWorks - Machine Learning
MACHINE LEARNING
A u t o m a t ic t e x t
c l a s s i f i c a t i o n b e l o n g s t o
t h e t e x t m i n i n g o r t e x t
a n a l y t i c s d o m a i n o f
m a c h i n e l e a r n i n g
C l a s s i f y i n g t e x t
i n p u t i n t o a s e t o f
p r e d e f i n e d
c a t e g o r i e s
A z u r e M L o f f e r s
t e m p l a t e t o
b u i l d t e x t
a n a l y t i c s
s o l u t i o n
SUPERVISED UNSUPERVISED
CLUSTERINGCLASSIFICATION RE GRE SSION
TEXT
CLASSIFICATION

#SitecoreSYM 13
How ItWorks – Supervised Learning
T R A I N I N G , T E X T,
D O C U M E N T S , I M A G E S ,
E T C .
L A B E L S
N E W T E X T,
D O C U M E N T,
I M A G E , E T C
M A C H I N E L E A R N I N G
A LG O R I T H M
E X P E C T E D L A B E L
FEATUREVECTORSFEATUREVECTORS
P R E D I C AT E M O D E L

#SitecoreSYM 14
Microsoft’s ML Products
Microsoft
ML Server
(Microsoft
R Server)
Azure
ML
Microsoft
Azure
Cognitive
Services
Microsoft
R
Microsoft
Cognitive
Toolkit
Batch AI
Training
Service
Spark MLLib
(HDInsight)
SQL ML
Services

#SitecoreSYM 15
Azure MLTemplate
START
STEP#1
DATA
PREPARATION
STEP#2
TEXT
PRE-
PROCESSING
STEP#3A
N-GRAMS TF
FEATURE
EXTRACTION
STEP#3B
UNIGRAMS
TF-IDF
FEATURE
EXTRACTION
STEP#4
TRAIN AND
EVALUATE
MODELS
STEP#5A
DEPLOY WEB
SERVICES WITH
N-GRAMS TF
MODEL
STEP#5B
DEPLOY WEB
SERVICES WITH
UNIGRAMS
TF-IDF MODEL

#SitecoreSYM
Demo –Azure ML
• Azure ML Studio
• Azure MLWeb Services
16

#SitecoreSYM
SitecorexConnect –SolutionOverview
17

#SitecoreSYM 18
How ItWorks – Sitecore xConnect API
S I T E C O R E I N G R E D I E N T S W H AT I T ’ S U S E D F O R
Collection Model The complete attendee profile is represented as an xConnect collection model
Contacts An xConnect contact is created as a representation of every attendee
Facets Extended information about the attendee is stored in custom contact facets
Channels Every social media platform is represented as a marketing channel
Interactions
Actions and activities of an attendee (e.g. tweets, posts, comments) are stored
via xConnect interactions

#SitecoreSYM 19
How ItWorks – Social Media + xConnect
S O C I A L M E D I A
I M P O R T E R
X C O N N E C T
C O L L E C T I O N
C O L L E C T I O N
D ATA B A S E

#SitecoreSYM 20
How ItWorks – Generating Recommendations
C O L L E C T I O N
D ATA B A S E
X C O N N E C T
C O L L E C T I O N
S E R V E R
S I T E C O R E
W I T H M L
C O M P O N E N T S
A Z U R E M L
W E B
S E R V I C E S
S Y M P O S I U M
A G E N D A
R E C O M M E N D A T I O N

#SitecoreSYM
How ItWorks –Sitecore xConnect
• Our data model: custom xConnect facets
• Social media integration
• Azure ML – web services integration
21

#SitecoreSYM
Behind the Scenes
22

#SitecoreSYM 23
Step 1: Define Custom Facets
[Serializable]
[FacetKey(FacetKeys.AttendeeProfile)]
public class AttendeeProfile : Facet
{
public string TwitterHandle { get; set; }
public Dictionary<string, double> TrackScores { get; } = new Dictionary<string, double>();
public Dictionary<string, double> TopicScores { get; } = new Dictionary<string, double>();
public int PostsProcessed { get; set; }
}

#SitecoreSYM 24
Step 1: Define Custom Facets
[Serializable]
[FacetKey(FacetKeys.SocialPostInfo)]
public class SocialPostInfo : Facet
{
public string Text { get; set; }
public DateTime CreatedAt { get; set; }
}

#SitecoreSYM 25
Step 2: Register the Facets in xConnect Model Configuration
public class CustomDataModel
{
public static XdbModel Model { get; } = BuildModel();
private static XdbModel BuildModel()
{
XdbModelBuilder xdbModelBuilder =
new XdbModelBuilder("Recommender", new XdbModelVersion(1, 1));
xdbModelBuilder.ReferenceModel(CollectionModel.Model);
xdbModelBuilder.DefineFacet<Contact, AttendeeProfile>("AttendeeProfile");
xdbModelBuilder.DefineFacet<Interaction, SocialPostInfo>("SocialPostInfo");
return xdbModelBuilder.BuildModel();
}
}

#SitecoreSYM 26
Step 2.5:Try to Save Data
The type of this instance does not correspond to any type in the model

#SitecoreSYM 27
Step 2.75: Register the Facets in xConnect Model Configuration
...
<schemas hint="list:AddModelConfiguration">
<schema name="customDataModel" type="Sitecore.XConnect.Client.Configuration.StaticModelConfiguration">
<param desc="modeltype">Recommender.Foundation.CollectionModel.CustomDataModel</param>
</schema>
</schemas>
...

#SitecoreSYM 28
Step 2.99:Try to Save Data
'CustomDataModel, 1.0' does not match the remote version

#SitecoreSYM 29
Step 3: Export the Model to Json
class Program
{
static void Main()
{
var json = XdbModelWriter.Serialize(CustomDataModel.Model);
File.WriteAllText(CustomDataModel.Model.FullName + ".json", json);
}
}

#SitecoreSYM 30
Step 3: Export the Model to Json
{
...
"Types": {
"Recommender.Foundation.CollectionModel.Facets.SocialPostInfo": {
...
"Type": "Facet",
"BaseType": "Sitecore.XConnect.Facet",
"Properties": {
"Text": {
"Type": "String"
},
"CreatedAt": {
"Type": "DateTime"
}
}
}
},
"Facets": [
...
{
"Target": "Interaction",
"Name": "SocialPostInfo",
"Type": "Recommender.Foundation.CollectionModel.Facets.SocialPostInfo"
}
]
}

#SitecoreSYM 31
Sitecore Forms Submit Action

#SitecoreSYM 32
Identify and Save the Contact
Tracker.Current.Session.IdentifyAs("twitter", twitterHandle);
contactManager.SaveContactToCollectionDb(Tracker.Current.Contact);

#SitecoreSYM 33
Load the Contact with Social Media Facets
ContactExpandOptions expandOptions = new ContactExpandOptions(
FacetKeys.AttendeeProfile,
FacetKeys.SocialImportInfo,
PersonalInformation.DefaultFacetKey);
IEntityReference<Contact> twitterContactReference =
new IdentifiedContactReference("twitter", twitterHandle);
Contact contact = client.Get(twitterContactReference, expandOptions);

#SitecoreSYM 34
Initialize Custom Attendee Facet
AttendeeProfile attendeeProfile =
contact.GetFacet<AttendeeProfile>() ?? new AttendeeProfile();
attendeeProfile.TwitterHandle = twitterHandle;
attendeeProfile.TrackScores.Clear();
attendeeProfile.TopicScores.Clear();
client.SetFacet<AttendeeProfile>(contact, attendeeProfile);

#SitecoreSYM 35
ImportTweets as Interactions
List<Status> results = TwitterHelper.GetUserTweets(context, twitterHandle);
foreach (Status tweet in results)
{
var interaction =
new Interaction(contact, InteractionInitiator.Contact);
var postInfo = new SocialPostInfo
{
CreatedAt = tweet.CreatedAt,
Text = tweet.FullText
};
client.SetFacet<SocialPostInfo>(interaction, postInfo);
client.AddInteraction(interaction);
}

#SitecoreSYM 36
ScoreAll Interactions Using Azure ML
public class AttendeeScorer
{
private readonly CognitiveServicesClient _services;
public AttendeeScorer(CognitiveServicesClient services)
{
_services = services;
}
public async Task ScoreAttendee(IXdbContext client, Contact contact)
{
try
{
const int batchSize = 10;
string text = string.Empty;
int i = 0;
List<SocialPostInfo> posts =
contact.Interactions
.Select(interaction => interaction.GetFacet<SocialPostInfo>())
.Where(p => p != null)
.ToList();
int maxi = posts.Count - 1;
AttendeeProfile profile = contact.GetFacet<AttendeeProfile>();
Dictionary<string, double> scores;
foreach (SocialPostInfo post in posts)
{
text += " " + post.Text;
if (++i % batchSize == 0 || i == maxi)
{
profile.PostsProcessed += i % batchSize == 0 ? batchSize : i % batchSize;
scores = await _services.InvokeTracksService(text);
foreach (string key in scores.Keys)
{
if (!profile.TrackScores.ContainsKey(key))
{
profile.TrackScores[key] = scores[key];
}
else
{
profile.TrackScores[key] += scores[key];
}
}
scores = await _services.InvokeTopicsService(text);
foreach (string key in scores.Keys)
{
if (!profile.TopicScores.ContainsKey(key))
{
profile.TopicScores[key] = scores[key];
}
else
{
profile.TopicScores[key] += scores[key];
}
}
text = String.Empty;
}
}
client.SetFacet(contact, profile);
}
catch (Exception ex)
{
Log.Error(ex.Message,ex, typeof(AttendeeScorer));
throw;
}
}
}
public class CognitiveServicesClient
{
public class StringTable
{
public string[] ColumnNames { get; set; }
public string[,] Values { get; set; }
}
public class ServiceOutput
{
public string Type { get; set; }
public ServiceOuputValue Value { get; set; }
}
public class ServiceOuputValue
{
public string[] ColumnNames { get; set; }
public string[] ColumnTypes { get; set; }
public string[][] Values { get; set; }
}
private static readonly Regex ClassRegex = new Regex(".+"(?<className>.+)".*");
public async System.Threading.Tasks.Task<Dictionary<string, double>> InvokeTracksService(string text)
{
ISitecoreContext context = SitecoreContextFactory.Default.GetSitecoreContext();
var endpoint = context.GetItem<CognitiveServicesEndpoint>("/sitecore/content/CognitiveServices/Tracks");
return await InvokeScoringService(text, endpoint);
}
public async System.Threading.Tasks.Task<Dictionary<string, double>> InvokeTopicsService(string text)
{
ISitecoreContext context = SitecoreContextFactory.Default.GetSitecoreContext();
var endpoint = context.GetItem<CognitiveServicesEndpoint>("/sitecore/content/CognitiveServices/Topics");
return await InvokeScoringService(text, endpoint);
}
public async System.Threading.Tasks.Task<Dictionary<string, double>> InvokeScoringService(string text, CognitiveServicesEndpoint endpoint)
{
using (var client = new HttpClient())
{
var scoreRequest = new
{
Inputs = new Dictionary<string, StringTable>() {
{
"Unigrams Web Service Input",
new StringTable()
{
ColumnNames = new[] {"text_column"},
Values = new[,]
{
{
text
}
}
}
},
},
GlobalParameters = new Dictionary<string, string>()
{
}
};
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", endpoint.ApiKey);
client.BaseAddress = new Uri(endpoint.EndpointUrl.Url);
HttpResponseMessage response = await client.PostAsJsonAsync("", scoreRequest).ConfigureAwait(false);
if (response.IsSuccessStatusCode)
{
string output = await response.Content.ReadAsStringAsync();
var result = JsonConvert.DeserializeObject<Dictionary<string, Dictionary<string, ServiceOutput>>>(output);
string[] columnNames = result["Results"].First().Value.Value.ColumnNames;
string[] values = result["Results"].First().Value.Value.Values[0];
Array.Resize(ref columnNames, columnNames.Length - 1);
Array.Resize(ref values, values.Length - 1);
return Enumerable.Range(0, columnNames.Length)
.ToDictionary(
i => ClassRegex.Match(columnNames[i]).Groups["className"].Value,
i => Convert.ToDouble(values[i], CultureInfo.InvariantCulture));
}
else
{
string responseContent = await response.Content.ReadAsStringAsync();
var ex = new Exception($"The request failed with status code: {response.StatusCode}. Full response: {responseContent}");
throw ex;
}
}
}
}

#SitecoreSYM
Experience Profile Demo
37

#SitecoreSYM
Let’s summarize…
38

#SitecoreSYM 39
Create a labeled dataset
Session titles + abstracts = training text
Session tracks & topics = Labels
Extract features using feature extraction algorithm
Train models in Azure ML
Deploy theTracks web service
Repeat steps 1-4 using session topics as labels and deploy theTopics web service
Step by Step – ML Solution
1
1
2
2
3
4
5

#SitecoreSYM 40
FetchTwitter posts using LinqToTwitter
Implement a custom xConnect model using contact and interaction facets
Use the xConnect Collection service to populate contacts and save social media interactions
Use theTracks andTopics Azure ML web services to batch-process social media interactions
Save label scores to a custom contact facet
Step by Step – Sitecore Solution: xConnect
2
3
4
5
1

#SitecoreSYM 41
Create a web form to get user input using Experience Forms
Import content using SPE by parsing JSON feed
Implement a calendar rendering to show full agenda
Use the highest-scoring track to generate initial agenda
This leaves us with conflicts or gaps in agenda
Resolve conflicts using topic scores (highest scoring topic wins)
Fill gaps using the secondary track along with the highest-scoring topics
Step by Step - Sitecore Solution - UI
1
1
2
3
4
2
3

#SitecoreSYM
What’s Next …?
42

#SitecoreSYM 43
Scalable Data-Processing platform designed to integrate with xDB
and Machine Learning workers
SitecoreCortex Launched
Provides default integration with Microsoft’s ML Server
BYOML (Bring your own ML)

#SitecoreSYM 44
SitecoreCortex Processing Engine
STEP#1
DATA
PREPARATION
Cortex Processing Engine
Task Agent
Worker
Data Source
Task Executor Storage
Model
Task Agent
Task Agent External
Service
MESSAGE
BUS
Event
Microsoft Machine
Learning Server

#SitecoreSYM 45
Remember Supervised Learning?
STEP#1
DATA
PREPARATION
T R A I N I N G , T E X T,
D O C U M E N T S ,
I M A G E S , E T C .
L A B E L S
M A C H I N E
L E A R N I N G
A LG O R I T H M
FEATUREVECTORS
P R E D I C AT E
M O D E L

#SitecoreSYM 46
With SitecoreCortex
BYOMLDATA PREPARATION
Distributed Task
WorkerData Source
Model
External
Service

#SitecoreSYM 47
SolutionWith Cortex Processing Engine
CONTINOUS SOCIAL
SYNC
SCORING LABELS USING
ML
INTERACTION FACETS CONTACT FACETS
ML MODEL TRAINING
Distributed Task
WorkerData Source
Model
MODEL FOR ML
Distributed Task
Worker
Data Source
Model

#SitecoreSYM
FOR DISCUSSION PURPOSESONLY. Sitecore Confidential
and Proprietary. © 2018 Sitecore Corporation A/S. All rights
reserved. Sitecore® and Own the Experience® are registered
trademarks of Sitecore Corporation A/S. All other brand and
product names are the property of their respective owners.
© 2001-2018 Sitecore Corporation A/S. All rights reserved. Sitecore® and Own the Experience® are registered
trademarks of Sitecore Corporation A/S. All other brand and product names are the property of their respective owners.
49

Sitecore: Session recommendation engine

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