Técnicas de recomendación y argumentación

TÉCNICAS DE RECOMENDACIÓN Y ARGUMENTACIÓN

AGENDA

• Técnicas de recomendación • Collaborative Filtering

• Content-based

• Knowledge-based

• Híbridas

• Técnicas de argumentación • Predicciones

• Recomendaciones

COLLABORATIVE FILTERING

• Valoraciones de todos los usuarios como características de los ítems

• Perfil de usuario • En base a las valoraciones del propio usuario

• Recomendaciones o predicciones • Por similaridad/probabilidad entre usuarios

• Por similaridad/probabilidad entre ítems

[Schafer, 2007]


• Universo • Valoraciones de U de los ítems en I

• Entrada • Valoraciones de u de los ítems en I

• Proceso • Identificar usuarios en U similares a u y extrapolar sus valoraciones

de i

[Burke, 2002]


• Predicciones • Valor numérico que representa la opinión anticipada del usuario

para el ítem no valorado. Conocido como Individual Scoring.

• Recomendaciones • Lista de N ítems que se corresponden con las preferencias del

usuario. Conocido como Top-N recommendation o Rank Scoring.

[Vozalis, 2003]

PREDICCIONES

[Zanker, 2010]

PREDICCIONES

[Zanker, 2010]

RECOMENDACIONES

9 Collaborative Filtering Recommender Systems 293

(Table 1). Without loss of generality, a ratings matrix consists of a table where each

row represents a user, each column represents a specific movie, and the number at the

intersection of a row and a column represents the user’s rating value. The absence of a

rating score at this intersection indicates that user has not yet rated the item.

Table 1. A MovieLens ratings matrix. Amy rated the movie Sideways a 5. Matt has not seen

The Matrix

The Matrix

Speed Sideways Brokeback Mountain

Amy 1 2 5

Matt 3 5 4

Paul 5 5 2 1

Cliff 5 5 5 5

The term user refers to any individual who provides ratings to a system. Most often,

we use this term to refer to the people using a system to receive information (e.g.,

recommendations) although it also refers to those who provided the data (ratings)

used in generating this information.

Collaborative filtering systems produce predictions or recommendations for a

given user and one or more items. Items can consist of anything for which a human

can provide a rating, such as art, books, CDs, journal articles, or vacation destinations.

Ratings in a collaborative filtering system can take on a variety of forms.

· Scalar ratings can consist of either numerical ratings, such as the 1-5 stars provided

in MovieLens or ordinal ratings such as strongly agree, agree, neutral, disagree,

strongly disagree.

· Binary ratings model choices between agree/disagree or good/bad.

· Unary ratings can indicate that a user has observed or purchased an item, or other-

wise rated the item positively. The absence of a rating indicates that we have no in-

formation relating the user to the item (perhaps they purchased the item somewhere

else).

Ratings may be gathered through explicit means, implicit means, or both. Explicit ratings are those where a user is asked to provide an opinion on an item. Implicit ratings are those inferred from a user’s actions. For example, a user who visits a

product page perhaps has some interest in that product while a user who subsequently

purchases the product may have a much stronger interest in that product. The issues of

design decisions and tradeoffs regarding collection of different types of ratings are

discussed in section 9.4.

9.1.2 The Beginning of Collaborative Filtering

As a formal area of research, collaborative filtering got its start as a means to handle

the shifting nature of text repositories. As content bases grew from mostly "official"

content, such as libraries and corporate document sets, to "informal" content such as

discussion lists and e-mail archives, the challenge of finding quality items shifted as

RECOMENDACIONES

• kNN

RECOMENDACIONES

• Similaridad

Euclidiana

Minkowski

Mahalanobis

Coseno

RECOMENDACIONES

[Vozalis, 2003]

PROBLEMAS

• Sparsity • Default Voting

• Insertar valoraciones por default

• User Average Scheme

[Vozalis, 2003]

PROBLEMAS

• Sparsity • Singular Value Decomposition (SVD)

[Amatriain, 2011]


• Escenarios para su utilización:

• Ítems que no requieran una exactitud semántica con las preferencias

del usuario

• Ítems con características heterogéneas y múltiples dominios

• Cuando se tengan valoraciones de los ítems

• Cuando no se conozcan las necesidades del usuario pero si sus preferencias

CONTENT-BASED

• Los ítems se definen por sus características • Género, año de publicación, autor, no. de páginas.

• Palabras clave.

• Perfil de (interés de) usuario • Características de los ítems valorados por el usuario.

• Recomendaciones • Comparación del ítem no valorado con el perfil de (interés de)

usuario.

CONTENT-BASED

• Universo • Características de los ítems en I

• Entrada • Características de los ítems en I valorados por u

• Proceso • Generar un clasificador que represente la conducta de valoraciones

de u y usarlo sobre cada i no valorado.

[Burke, 2002]

CONTENT-BASED

• Técnicas 1. Representación de ítems

2. Creación del perfil (de interés) de usuario - Clasificador

3. Similaridad de (1) con (2)

CONTENT-BASED

• Representación de ítems

Content-based recommendation systems analyze item descriptions to identify items

that are of particular interest to the user. Because the details of recommendation

systems differ based on the representation of items, this chapter first discusses

alternative item representations. Next, recommendation algorithms suited for each

representation are discussed. The chapter concludes with a discussion of variants of

the approaches, the strengths and weaknesses of content-based recommendation

systems, and directions for future research and development.

10.1.1 I tem Representation

Items that can be recommended to the user are often stored in a database table. Table

10.1 shows a simple database with records (i.e., “rows”) that describe three

restaurants. The column names (e.g., Cuisine or Service) are properties of restaurants.

These properties are also called “attributes,” “characteristics,” “fields,” or “variables”

in different publications. Each record contains a value for each attribute. A unique

identifier, ID in Table 10.1, allows items with the same name to be distinguished and

serves as a key to retrieve the other attributes of the record.

Table 10.1. A restaurant database

ID Name Cuisine Service Cost

10001 Mike’s Pizza Italian Counter Low

10002 Chris’s Cafe French Table Medium

10003 Jacques Bistro French Table High

The database depicted in Table 10.1 could be used to drive a web site that lists and

recommends restaurants. This is an example of structured data in which there is a

small number of attributes, each item is described by the same set of attributes, and

there is a known set of values that the attributes may have. In this case, many machine

learning algorithms may be used to learn a user profile, or a menu interface can easily

be created to allow a user to create a profile. The next section of this chapter

discusses several approaches to creating a user profile from structured data.

Of course, a web page typically has more information than is shown in Table 10.1,

such as a text description of the restaurant, a restaurant review, or even a menu. These

may easily be stored as additional fields in the database and a web page can be

created with templates to display the text fields (as well as the structured data).

However, free text data creates a number of complications when learning a user

profile. For example, a profile might indicate that there is an 80% probability that a

particular user would like a French restaurant. This might be added to the profile

because a user gave a positive review of four out of five French restaurants.

However, unrestricted text fields are typically unique and there would be no

opportunity to provide feedback on five restaurants described as “A charming café

with attentive staff overlooking the river.”

An extreme example of unstructured data may occur in news articles. Table 10.2

shows an example of a part of a news article. The entire article can be treated as a

large unrestricted text field.

Table 10.2. Part of a newspaper article

Lawmakers Fine-Tuning Energy Plan SACRAMENTO, Calif. -- With California's energy reserves remaining all but

depleted, lawmakers prepared to work through the weekend fine-tuning a plan Gov.

Gray Davis says will put the state in the power business for "a long time to come."

The proposal involves partially taking over California's two largest utilities and

signing long-term contracts of up to 10 years to buy electricity from wholesalers.

Unrestricted texts such as news articles are examples of unstructured data. Unlike

structured data, there are no attribute names with well-defined values. Furthermore,

the full complexity of natural language may be present in the text field including

polysemous words (the same word may have several meanings) and synonyms

(different words may have the same meaning). For example, in the article in Table

10.2, “Gray” is a name rather than a color, and “power” and “electricity” refer to the

same underlying concept.

Many domains are best represented by semi-structured data in which there are

some attributes with a set of restricted values and some free-text fields. A common

approach to dealing with free text fields is to convert the free text to a structured

representation. For example, each word may be viewed as an attribute, with a

Boolean value indicating whether the word is in the article or with an integer value

indicating the number of times the word appears in the article.

Many personalization systems that deal with unrestricted text use a technique to

create a structured representation that originated with text search systems [34]. In this

formalism, rather than using words, the root forms of words are typically created

through a process called stemming [30]. The goal of stemming is to create a term that

reflects the common meaning behind words such as “compute,” “computation,”

“computer” “computes” and “computers.” The value of a variable associated with a

term is a real number that represents the importance or relevance. This value is called

the tf* idf weight (term-frequency times inverse document frequency). The tf* idf

weight, w(t,d), of a term t in a document d is a function of the frequency of t in the

document (tft,d), the number of documents that contain the term (dft) and the number

of documents in the collection (N).1

1 Note that in the description of tf* idf weights, the word “document” is traditionally used since

the original motivation was to retrieve documents. While the chapter will stick with the

original terminology, in a recommendation system, the documents correspond to a text

description of an item to be recommended. Note that the equations here are representative of

the class of formulae called tf* idf. In general, tf* idf systems have weights that increase

monotonically with term frequency and decrease monotonically with document frequency.

[Pazzani, 2007]

CONTENT-BASED

• Representación de ítems • Vector Space Model (VSM)

• Keyword-based VSM

• Synset-based VSM

• Semantic analysis

• Ontologies

• Explicit Semantic Analysis (ESA)

REPRESENTACIÓN DE ÍTEMS

• Vector Space Model • Representación espacial de las características del ítem

• Aplicado a ítems de tipo texto o con características textuales

• NO-ESTRUCTURADO


• Keyword-based VSM • Basado en técnicas de recuperación de información

• TF-IDF (Term Frequency – Inverse Document Frecuency)


• Keyword-based VSM • En base a observaciones empíricas, se observan 3 supuestos:

• IDF assumption: Los términos poco frecuentes no son menos relevantes que los frecuentes.

• TF assumption: Múltiples ocurrencias de un término en un documento no con menos relevantes que las ocurrencias simples.

• Normalization assumption: Los documentos largos no se prefieren sobre los cortos.

[Lops, 2011]


• Keyword-based VSM Table 10.2. Part of a newspaper article

Lawmakers Fine-Tuning Energy Plan SACRAMENTO, Calif. -- With California's energy reserves remaining all but

depleted, lawmakers prepared to work through the weekend fine-tuning a plan Gov.

Gray Davis says will put the state in the power business for "a long time to come."

The proposal involves partially taking over California's two largest utilities and

signing long-term contracts of up to 10 years to buy electricity from wholesalers.

Unrestricted texts such as news articles are examples of unstructured data. Unlike

structured data, there are no attribute names with well-defined values. Furthermore,

the full complexity of natural language may be present in the text field including

polysemous words (the same word may have several meanings) and synonyms

(different words may have the same meaning). For example, in the article in Table

10.2, “Gray” is a name rather than a color, and “power” and “electricity” refer to the

same underlying concept.

Many domains are best represented by semi-structured data in which there are

some attributes with a set of restricted values and some free-text fields. A common

approach to dealing with free text fields is to convert the free text to a structured

representation. For example, each word may be viewed as an attribute, with a

Boolean value indicating whether the word is in the article or with an integer value

indicating the number of times the word appears in the article.

Many personalization systems that deal with unrestricted text use a technique to

create a structured representation that originated with text search systems [34]. In this

formalism, rather than using words, the root forms of words are typically created

through a process called stemming [30]. The goal of stemming is to create a term that

reflects the common meaning behind words such as “compute,” “computation,”

“computer” “computes” and “computers.” The value of a variable associated with a

term is a real number that represents the importance or relevance. This value is called

the tf* idf weight (term-frequency times inverse document frequency). The tf* idf

weight, w(t,d), of a term t in a document d is a function of the frequency of t in the

document (tft,d), the number of documents that contain the term (dft) and the number

of documents in the collection (N).1

1 Note that in the description of tf* idf weights, the word “document” is traditionally used since

the original motivation was to retrieve documents. While the chapter will stick with the

original terminology, in a recommendation system, the documents correspond to a text

description of an item to be recommended. Note that the equations here are representative of

the class of formulae called tf* idf. In general, tf* idf systems have weights that increase

monotonically with term frequency and decrease monotonically with document frequency.


• Keyword-based VSM • Stemming

• Ocurrencia de palabras tomando la raíz del término

• Computación, Computadora, Computólogo, Cómputo


• Synset-based VSM • Utiliza los synsets de WordNet

• Mismos cálculos que Keyword-based VSM

• Los pesos de los términos del mismo synset forman un elemento del vector

• Los 3 supuestos aplican de la misma forma

• Propósito:

• Dar tratamiento a la sinonimia y polisemia


• Semantic Analysis • Adición de información semántica a las representaciones de los

ítems

• Uso de Ontologías


• Explicit Semantic Analysis • Añade información de fuentes de conocimiento externas como

Wikipedia, Open Directory Project (ODP) y Yahoo! Web Directory.

• Cada concepto de la ontología se encuentra ligado a un enlace de la fuente externa

[Gabrilovich, 2011]

CONTENT-BASED

• Creación del perfil (de interés) de usuario • Vector Space Model(VSM)

• Support Vector Machine (SVM)

• Métodos probabilísticos (Naïve Bayes)

• Relevance Feedback (Rochio’s Algorithm)

CREACIÓN DEL PERFIL DE USUARIO

• Vector Space Model • Mismo caso que el VSM para la representación de ítems

• Vector con las características de todos los ítems valorados por el usuario


• Support Vector Machine • Encontrar una función (hiperplano lineal, límite de decisión) que

separe los datos con un margen maximizado.


• Support Vector Machine


• Métodos probabilísticos (Naïve Bayes)

Multinomial Event Model


• Relevance Feedback (Rochio’s Algorithm) • Similar al VSM

• Crea un vector prototipo para compararlo con el vector de características del ítem

CONTENT-BASED

• Similaridad

Euclidiana

Minkowski

Mahalanobis

Coseno

CONTENT-BASED


• Ítems con contenido textual (Objetos de conocimiento)

• Ítems con descripciones textuales

• Ítems que con el mismo conjunto de características y valores discretos para cada rasgo (Sin heterogeneidad de ítems)

• Cuando sea necesario relacionar un ítem con una categoría

KNOWLEDGE-BASED

• Se basa en inferencias sobre las necesidades y preferencias del usuario

• Diferencia • Utiliza conocimiento funcional

• Como un determinado ítem cumple una necesidad específica

• El perfil de usuario debe ser una estructura de conocimiento que soporte la inferencia

[Burke, 2002]

KNOWLEDGE-BASED

• Universo • Características de los ítems en I

• Conocimiento sobre como estos ítems cumplen las necesidades de los usuarios (Reglas de inferencia)

• Entrada • Descripción de los intereses o necesidades de u

• Proceso • Inferir una relación entre i y las necesidades de u

[Burke, 2002]

KNOWLEDGE-BASED

• Association Rule Mining • Identifica colecciones de dos o mas elementos con un número

repetitivo de transacciones que los contienen.

• Itemset = Colecciones (Milk, Beer, Diaper)

• Support count = Ocurrencias/Transacciones

• Frequent itemset = Colecciones con Support count superior o igual al umbral

• Association rule = X -> Y (Milk, Diaper) -> Beer

• Se hacen N combinaciones y se comparan con el umbral mínimo de transacciones.

[Amatriain, 2011]

KNOWLEDGE-BASED

• Rule-based classifier • Se basa en el uso de las reglas de asociación

• If … then …

KNOWLEDGE-BASED

• Rule-based classifier

ExampleExample

Pow ershot XY

Brand Canon

W eight LHS RHS

C1: 25 TRUE Brand = Brand pref.

Know ledge Base: Product catalogue:

Brand Canon

Lower focal length 35

Upper focal length 140

C1: 25 TRUE Brand Brand pref.

C2: 20 Motives = Landscape Low. foc. Length =<28

C3: 15 TRUE Price =< Max cost

Price 420 EUR

Lum ix

C3: 15 TRUE Price =< Max. cost

Current user:

User m odel

( requirem ents)

Motives Landscape

Lum ix

Brand Panasonic

Lower focal length 28Motives Landscape

Brand preference Canon

Max. cost 350 EUR


Price 319 EUR

- 59 -© Dietmar Jannach and Markus Zanker

ExampleExample

Pow ershot XY

Brand Canon

W eight LHS RHS



Brand Canon






Price 420 EUR

Lum ix


Current user:

User m odel

( requirem ents)

Motives Landscape

Lum ix

Brand Panasonic



Max. cost 350 EUR


Price 319 EUR


ExampleExample

Pow ershot XY

Brand Canon

W eight LHS RHS



Brand Canon






Price 420 EUR

Lum ix


Current user:

User m odel

( requirem ents)

Motives Landscape

Lum ix

Brand Panasonic



Max. cost 350 EUR


Price 319 EUR


KNOWLEDGE-BASED

• Rule-based classifier

• Rank 1. Lumix 35/60

• Rank 2. Powershot 25/60

[Zanker, 2010]

KNOWLEDGE-BASED


• Cuando se han obtenido las reglas asociativas mediante el análisis previo del dominio

• Cuando no se tienen valoraciones de los ítems

• Cuando se cuente con información de las necesidades del usuario y del dominio de los ítems

HÍBRIDAS

• Métodos de hibridación • Weighted

• Switching

• Cascade

• Mixed

• Feature Augmentation

[Burke, 2002]

HÍBRIDAS

• Weighted

[Zanker, 2010]

HÍBRIDAS

• Switching

• Por ejemplo: • Si hay pocas valoraciones

• Usar Knowledge-based

• Si no

• Usar Collaborative Filtering

[Zanker, 2010]

HÍBRIDAS

• Cascade

[Zanker, 2010]

HÍBRIDAS

• Mixed • Las recomendaciones de distintos recomendadores se presentan al

mismo tiempo

• Feature Augmentation • La salida de un recomendador es la entrada del siguiente.

COMBINACIONES

[Burke, 2002]

ARGUMENTACIÓN

• Predicciones • Data-Explorative Model

• El usuario puede observar los datos sobre los cuales se hizo la predicción.

• No se basa en el proceso matemático para la obtención de la predicción.

• Process-Explorative Model

• Se explica el proceso matemático tras la predicción.

• Diagrama de flujos

• Argumentative Model

• Utiliza técnicas de argumentación lógica para soportar la predicción.

ARGUMENTACIÓN

• Recomendaciones • Estilos de recomendación

• Por tipo de elementos (user x feature x item)

• Por visualización

ARGUMENTACIÓN

• Por tipo de elemento

[Papadimitriou, 2011]

ARGUMENTACIÓN

• Por tipo de elemento

[Papadimitriou, 2011]

ARGUMENTACIÓN

• Por tipo de visualización • Keyword Style Explanation

• Neighbor Style Explanation

• Influence Style Explanation

[Bilgic, 2005]

REFERENCIAS

[Burke, 2002] Burke, Robin. «Hybrid Recommender Systems: Survey and Experiments.» User Modeling and User-Adapted Interaction, nº 12 (2002): 331-370.

[Schafer, 1999] Schafer, J. B., J. Konstan, y J. Riedl. «Recommender Systems in E-Commerce.» Proceedings of the First ACM Conference on Electronic Commerce. Denver, Colorado, 1999. 158-166.

[Pasquale, 2011] Lops, Pasquale, Marco de Gemmis, y Giovanni Semeraro. «Content-based Recommender Systems: State of the Art and Trends.» En Recommender Systems Handbook, de Francesco Ricci, Lior Rokach, Bracha Shapira y Paul B. Kantor, 73-105. Springer Science+Bussiness Media, 2011.

[Pazzani, 2007] Pazzani, Michael J., y Daniel Billsus. «Content-based Recommendation Systems.» En Personalization, The Adaptative Web: Methods and Strategies of Web, de Peter Brusilovsky, Alfred Kobsa y Wolfgang Nejdl, 325-341. Springer-Verlag Berlin Heidelberg, 2007.

REFERENCIAS

[Gabrilovich, 2007] Gabrilovich, E., Markovitch, S.: Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis. In: M.M. Veloso (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 1606–1611 (2007)

[Schafer, 2007] Schafer, J. B., Frankowski, D., Herlocker, J. y S. Shilad. «Collaborative Filtering Recommender Systems.» En Personalization, The Adaptative Web: Methods and Strategies of Web, de Peter Brusilovsky, Alfred Kobsa y Wolfgang Nejdl, 325-341. Springer-Verlag Berlin Heidelberg, 2007.

[Vozalis, 2003] Vozalis, E., y K. G. Margaritis. «Analysis of Recommender Systems' Algorithms.» 6th Hellenic European Conference on Computer Mathematics & its Applications HERCMA. Atenas, Grecia, 2003.

REFERENCIAS

[Zanker, 2010] Zanker, M., y D. Jannach. «Introduction to Recommender Systems.» Tutorial at ACM Symposium on Applied Computing. Sierre, Suiza, 2010.

[Amatriain, 2011] Amatriain, X., Jaimes, A., Oliver, N. y Pujol, J. M. «Data Mining Methods for Recommender Systems» En Recommender Systems Handbook, de Francesco Ricci, Lior Rokach, Bracha Shapira y Paul B. Kantor, 73-105. Springer Science+Bussiness Media, 2011.

[Papadimitriou, 2011] Papadimitriou, Alexis, Panagiotis Symeonidis, y Yannis Manolopoulos. «A generalized taxonomy of explanations styles for traditional and social recommender systems.» Data Mining and Knowledge Discovery , 2011: 1-29.

REFERENCIAS

[Bilgic, 2005] Bilgic, M., y R. J. Mooney. «Explaining Recommendations: Satisfaction vs. Promotion.» International Conference on Intelligent User Interfaces. San Diego, California, 2005.

Technology

Técnicas de recomendación y argumentación