Towards a framework that allows using acognitive architecture to personalize

recommendations in e-commerce

Jordi Sabater-Mir, Joan Cuadros and Pere Garcia

IIIA – Artificial Intelligence Research InstituteCSIC – Spanish National Research Council

Motivation

• A single recommender technique cannot be enough to cover all the real necessities of the dierent users that are looking for a recommendation.

• The solution to this problem: let's take a full “palette” of recommender techniques and, like a painter, use at run time the right “color” for each situation (properly fine tuned to recapture the nuances of the context).

Motivation

• What are we looking for is an automated decision maker that can decide: – which is the best recommendation technique (or

techniques) to use, how to combine them,– how to adapt the results to the specic needs of a

given user and – how to properly show the results to that user.

• Our proposal is to use a cognitive architecture as such a decision maker.

Motivation

• Take to the next level the notion of “personalization” in recommendation.

Beliefs

Desires

User profile

Use

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Planslibrary

Intentions

Data analysisPr

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Text files

UI

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Cognitive architecture

Textfiles xml

Agent speak

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User Interface

Recommenders

Set of Recommenders

Domain Ontology General Knowledge

BDI-Engine

Interpreter

The framework

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Data analysisPr

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Text files

UI

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Textfiles xml

Agent speak

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User Interface

Recommenders

Set of Recommenders

Domain Ontology General Knowledge

Beliefs

Desires

User profile

Planslibrary

Intentions

Cognitive architecture

BDI-Engine

Interpreter

• Knows about the user needs and his/her desires, goals, restrictions, beliefs, etc.

• Knows about the recommenders that are available, their strengths and weaknesses.

• Tries to stisfy the user needs.

• Advanced capabilities: justification and argumentation

• For certain tasks it is crucial that the behavior of the machine be similar to that of a human.

• Trust has to be build on top of the mutual understanding.

• To achieve this we think the most natural way is to use cognitive architectures.

The framework

Beliefs

Desires

User profile

Use

r Cog

nitiv

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Planslibrary

Intentions

Data analysisPr

e-po

cess

ing

Text files

UI

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Cognitive architecture

Textfiles xml

Agent speak

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User Interface

Recommenders

Set of Recommenders

Domain Ontology General Knowledge

BDI-Engine

Interpreter

Cleaning, parsing, normalizing… raw data.Statistical measures to advanced data mining algorithms.

• Represent the information in terms of the cognitive architecture.

• Qualitative high level concepts -> “This user is a very good client”

The framework

Beliefs

Desires

User profile

Use

r Cog

nitiv

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ofile

Planslibrary

Intentions

Data analysisPr

e-po

cess

ing

Text files

Use

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file

Raw

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Cognitive architecture

Textfiles xml

Agent speak

Expe

rts/

Ded

ucer

s

Recommenders

Set of Recommenders

Domain Ontology General Knowledge

BDI-Engine

Interpreter

UI

User Interface

The framework

Use

r Cog

nitiv

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ofile

Data analysisPr

e-po

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Text files

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Raw

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Textfiles xml

Agent speak

Expe

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Recommenders

Set of Recommenders

Domain Ontology General Knowledge

UI

User Interface

Beliefs

Desires

User profile

Planslibrary

Intentions

Cognitive architecture

BDI-Engine

Interpreter

The framework

Cognitive architecture

• Receive a notication from the user expressing the willingness to obtain a recommendation of a certain kind.

• Analyze if that request is coherent with what it knows about the user and decide what is the best action to perform.

• Query one or several recommenders that, given the knowledge the cognitive architecture has about them, can provide an answer that will satisfy the user's request.

• Adapt, combine, modify the recommendations received from the recommenders to personalize the nal answer to the user.

• Show the processed answer to the user in a personalized way.

Basic functionality: It has to be able to…

Cognitive architecture

• Establish a dialog with the user. For example, if the request from the user is not coherent with what the cognitive architecture knows about him/her, establish a dialog with the user to express the problem and be able to adapt the internal knowledge based on what the user explicitly states during that dialog.

• Justify the recommendations. The justification has to be adapted to each kind of user. Some users prefer simple and short justications while others want all the details.

• Participate in an argumentation process with the user where both, the user and the cognitive architecture can express arguments and counterarguments to achieve a consensus about a recommendation.

• Incorporate automatically new recommenders to the palette of recommenders and be able to use them without external intervention. This ambitious capa bility requires the description of each recommender in terms of its strengths and weaknesses.

Advanced functionality: …and also…

Beliefs

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User profile

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Planslibrary

Intentions

Data analysisPr

e-po

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Text files

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Cognitive architecture

Textfiles xml

Agent speak

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Domain Ontology General Knowledge

BDI-Engine

Interpreter

UI

User Interface

Recommenders

Set of Recommenders

Static vs Dynamic

The framework

Beliefs

Desires

User profile

Use

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Planslibrary

Intentions

Data analysis

Pre-

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Text files

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Cognitive architecture

Textfiles xml

Agent speak

Expe

rts/

Ded

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sUser Interface

Recommenders

Set of Recommenders

Domain Ontology General Knowledge

BDI-Engine

Interpreter

Python JavaBDI-Engine: Jason

HTML 5, CSS3, javascriptPhoneGap

Jquery Mobile

JavaCF: Mahout

Expert system: Drools

Web serviceRESTful

Proof of concept

OWL

Scenario

• Mary always buys the same yogurts. She is a little bit tired of eating the same yogurt one day after another and today she wants to try something new.

• She goes to the supermarket and once in the dairy products aisle, takes the smartphone and scans the barcode of her favorite yogurt.

• She asks to the system for an alternative. • The system processes her request and suggests an

alternative that could satisfy her requirements.

Beliefs

Desires

User profile

Use

r Cog

nitiv

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ofile

Planslibrary

Intentions

Data analysisPr

e-po

cess

ing

Text files

UI

Use

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file

Raw

Dat

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Cognitive architecture

Textfiles xml

Agent speak

Expe

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User Interface

Recommenders

Set of Recommenders

Domain Ontology General Knowledge

BDI-Engine

Interpreter

Proof of concept

Raw data and Pre-processing:

Set of text files coming directly from points of sale terminals (POS) from the Alimerka supermarket chain.

More than 900000 files coming from 176 supermarkets covering a period of 18 months.

The files are grouped by supermarket (one file per supermarket per day) and contain purchases, promotions, coupons, loyalty points, etc.

Actions:Clean dataSelect only relevant fieldsOrganise purchases by client instead of by

supermarket

In our use-case we have restricted to 500 the number of clients and the time period to 12 months.

Data processing:

Prepare the user files for a recommender based on collaborative filtering.

Because Mary is looking for an alternative to a product, in order to prepare the data for the recommender we have to use only those lines that refer to products that are in the same subtree in the ontology that the reference product.

Our recommender will compare Mary with the other 499 users, will establish a similarity among them and will use what the most similar clients have bought that Mary has not as a recommendation.

The process of filtering the client files has to be done at run time.

Beliefs

Desires

User profile

Use

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Planslibrary

Intentions

Data analysisPr

e-po

cess

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Text files

Use

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file

Raw

Dat

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Dat

a M

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Proc

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d D

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Cognitive architecture

Textfiles xml

Agent speak

Expe

rts/

Ded

ucer

s

Recommenders

Set of Recommenders

Domain Ontology General Knowledge

BDI-Engine

Interpreter

UI

User Interface

Proof of concept

Technologies:PhonegapJquery mobile

(a) (b) (c) (d)

a) Mary logs into the systemb) Selects which kind of recommendation wantsc) Scans the reference product bar coded) Receives the recommendation

Use

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Data analysisPr

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Textfiles xml

Agent speak

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Recommenders

Set of Recommenders

Domain Ontology General Knowledge

UI

User Interface

Beliefs

Desires

User profile

Planslibrary

Intentions

Cognitive architecture

BDI-Engine

Interpreter

Proof of concept

+get_alternative(ID, X) : true <-

ask_collaborative_filtering(ID, X).

get_alternative(id, productSel).

Recommenders

Set of Recommenders

possible_rec(id, list_of_products).

+possible_rec(ID, Y) : get_alternative(ID, X)<- -possible_rec(ID, Y);

+rec(ID, Y).

+rec(ID, [Head|Tail]) <- send_rec(Head).

Beliefs

Desires

User profile

Use

r Cog

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Planslibrary

Intentions

Data analysisPr

e-po

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Text files

Use

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Raw

Dat

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Cognitive architecture

Textfiles xml

Agent speak

Expe

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Domain Ontology General Knowledge

BDI-Engine

Interpreter

UI

User Interface

Recommenders

Set of Recommenders

Proof of concept

A tipical collaborative filtering recommender.

Mahout library.

Thanks for your attention.