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Challenges and Conclusions

Francesco RiccieCommerce and Tourism Research LaboratoryAutomated Reasoning Systems DivisionITC-irstTrento – Italyricci@itc.ithttp://ectrl.itc.it

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Content

� A bidimensional model of recommendation

� A taxonomy of recommender systems

� Architectures for recommender systems

� Recommender systems vs IR

� RS and user modeling

� Scalability

� Reactive vs. proactive

� Memory- vs. model-based

� Privacy

� Evaluation

� Challenges

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Personalization

� “Personalization is the ability to provide content and services

tailored to individuals based on knowledge about their

preferences and behavior” [Paul Hagen, Forrester Research,

1999];

� “Personalization is the capability to customize customer

communication based on knowledge preferences and

behaviors at the time of interaction [with the customer]” [Jill

Dyche, Baseline Consulting, 2002];

� “Personalization is about building customer loyalty by building

a meaningful one-to-one relationship; by understanding the

needs of each individual and helping satisfy a goal that

efficiently and knowledgeably addresses each individual’s

need in a given context” [Doug Riecken, IBM, 2000].

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Recommender Systems Summary

� Original goal: A recommender system helps the user to make choices when there is no sufficient personal experience of the available options.

� But, in practice, research has focused on:

– Coping with Information overload: the system filters irrelevant content or rank relevant content (according to a user model).

– Personalized Information Retrieval: the user is supposed to actively search for relevant content.

– Applications to simple mass market products:CDs, movies, books, etc.

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“Core” Recommendation Techniques

[Burke, 2002]

U is a set of usersI is a set of items/products

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Comparison of different Techniques

[Burke 2002]

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A Simplified Model of Recommendation

1. Two types of entities: Users and Items

2. A background knowledge:

� A set of ratings: a map R: Users x Items � [0,1] U

{?}

� A set of “features” of the Users and/or Items

3. A method for eliminating all or part of the ‘?’ values

for some (user, item) pairs – substituting ‘?’ with the

true values

4. A method for selecting the items to recommend

� Recommend to u the item i*=arg maxi∈Items {R(u,i)}

[Adomavicius et al., 2005]

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A Bidimensional Model

user

item

ratings

User features

Product features

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Collaborative Filtering

user

item

ratings

4 out of 5

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Content Based Filtering (Classical)

user

item

ratings

Product features

Uses only the ratings of the target (active) user

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Demographic

user

item

ratings

User features

Demographic features of the user

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Utility and simple CBR

user

item

User features

Product features

Utility weights for the same features of the

products

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ratings

Knowledge-Based

user

item

User features

Product features

Rich user and product profiles and complex relationships between the two models

relations

relations

relations

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Filtering as a Classification Problem

� The recommender task is a function approximation -

classification (regression) problem

– F(user, item) = F(u1, …, un,i1, …,im)� {like, dislike}

UM IMF( ),

Dislike

Like

� Identity: demographic, space-time position (psychology ?).

� Interaction History:

– Short Term (Session): goals, motivations, clicks (dynamic, explicit/implicit)

– Long Term: objects bought, topics discussed, …

� Content-related Model: application dependent preferences. The same language used to describe the content.

UM

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Not Just a Classification Task

� User support cannot be reduced to the system guess of the correct “like/dislike” function F(user, content) �{like, dislike}

� Complexity dimensions (two ex.):

– Interactivity: sequential guesses of “F” calls; contextual question answering; games; …

– User Interface: not only content presentation –major influence on recommendation acceptance (language, media, …); usability.

? If the abstract model of a recommender system is much more complex then how to measure the impact of all these elements on the user (satisfaction and task execution)?

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A Taxonomy for Recommender Applications

Recommendation MethodRaw retrieval

Manually selectedStatistical summarization

Attribute-basedItem-to-Item correlation(association rule)

User-to-User correlation(collaborative filtering)

E-Store Engine

user

Targeted Customer InputsImplicit/Explicit Navigation

Keyword/ItemAttributeRatings

Purchase History

Community InputsItem Attribute

External Item PopularityPurchase History

RatingsText Comments

OutputsSuggestionPredictionRatingsReviews

DeliveryPushPull

Passive

Degree of PersonalizationNon

EphemeralPersistent

Response/Feedback Response/Feedback

[Shafer et al. 2001]

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Application Models (recommendation goals)

� Helping New and Infrequent Visitors

– Broad Recommendation Lists (overall best sellers, best sellers in category, editor and expert recommendations, and other collections of product)

� Building Credibility through Community

– Customer comments and ratings(leap over the credibility hurdle to move towards one-to-one relationship -> collect review and rating from members of the community at large)

� Inviting Customer Back

– Notification Services

� Cross-Selling

– Product-Associated Recommendations

� Building Long-Term Relationships

– Deep Personalization

[Shafer et al. 2001]

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Application Models and Context

� The concept of “application model” ([Shafer et al.,

2001]) is raising the issue that personalization is not

only concerned with “individualization”

� Personalization must be dependent on the

– User task/goal

– The context of the execution

– The nature (quantity and quality) of available data

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Example

� Searching a restaurant

� XXXX

– In New York or in Trento; With a PDA or with a

mobile phone; for a person or a group, for now or

for tonight; the best for the price …

� YYYY

– I like spaghetti more that pizza; I prefer eating in

the park; I want to eat in max 30 mins, ..

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Contextual Computing

� Contextual computing refers to the enhancement of a

user’s interactions by understanding the user, the context,

and the applications and information being used, typically

across a wide set of user goals

� Actively adapting the computational environment - for each

and every user - at each point of computation

� Contextual computing approach focuses on understanding the

information consumption patterns of each user

� Contextual computing focuses on the process not only on the

output of the search process.

[Pitkow et al., 2002]

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Contextualization and Individualization

� Contextualization: the interrelated conditions that occur

within an activity

– It includes factors like the nature of information available,

the information currently being examined, the applications

in use, when, and so on

� Individualization: the totality of characteristics that

distinguishes an individual

– It encompasses elements like the user’s goals, prior and

tacit knowledge, past information-seeking behaviors,

among others

� Personalization must focus on the combination of the

user and the context within the application of search.

[Pitkow et al., 2002]

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Application Domains

� Netnews

� Movies

� Web pages

� Documents

� Travel

� Email (filtering)

� Music

� Web search

� E-commerce: cameras,

printers, pc, …

[Montaner et al., 2003]

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Profile Representation

� Purchase history

� Weighted feature vector

� Booleans feature vector

� Clusters

� Ratings

� Semantic network

� Decision tree

� Probabilistic feature vector

� Demographic information

� N-grams

� Rules (induced)

� Frequent itemsets

[Montaner et al., 2003]

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Profile Learning

� Rule induction

� Feature selection

� TF-IDF

� Reinforcement learning

� Clustering

� Neural network

� Winnow

[Montaner et al., 2003]

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Classification of Personalization Approaches

[Adomavicius and Tuzhilin, 2005]

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Consumer, Provider and Market

� Provider-centric is the most common approach to personalization (recommender systems embedded in the merchant site – travelocity, amazon, …)

� Consumer-centric assumes each consumer has his or her own personalization engine (or agent) that “understands” this particular consumer and provides personalization services across several providers (PocketLens [Miller et al., 2004], e-Butler [Adomavicius and Tuzhilin, 2002])

� Market-centric provides personalization services for a specific marketplace in a particular industry or sector. The personalization engine is the infomediary, knowing the needs of the consumer and the provider’s offerings and trying to match them in the best ways possible according to their internal goals (Trip@dvisor).

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Architectures for Recommender Systems

� We may imagine a range of architectures that

generalize those illustrated by [Adomavicius and

Tuzhilin, 2005]

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Preferences

Strategies

Needs (information)

Cooperative Recommender

Hypotheses on user

Preferences

Strategies

Needs (information)interaction

Cooperative

Suppliers

content

Delegation

Payoff

Delegation

Payoff

Update

user model

query

interaction

interaction

contentservices

contentservices

contentservices

Delegation

Payoff

Update prefs,

strategy and needs

content

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Interaction

PlannerBelieves

Decision

MakingLearning

GUI Co

mm

un

ication

Interaction

PlannerBelieves

Decision

MakingLearning

GUI Co

mm

un

ication

End Users Intermediaries Suppliers

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Mediating Mechanism(flow for initializing UMs)

Service 1

Service L

…

Website 1

…

Website M

User 1

User N

…

Mediating Mediating

mechanismmechanism

System that needs

User Model for a

personalization

UM

Knowledge base

[Berkowsky, 2006]

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Example: Cross-Technique Mediation

� Mediation from Collaborative to Content-Based User Models

� Input: List of explicit ratings on items (a CF user model of thetarget user)

– UMCF={item:rating}

– “The Lord of The Rings”:1, “The Matrix”:0.8, “Psycho”:0.2, “Friday the 13th”:0, “Star Wars”:0.9,

� Output: Weighted list of preferred features of the target user (to be exploited in a content-based recommender system)

– UMCB={feature:level_of_preference}

– science-fiction:0.9, horror:0.1

[Berkovsky et al., 2006b]

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Phases of Personalization

� Collection of web data: implicit and explicit user data (web

server, cookies, session tracking)

� Preprocessing of Web data: structuring and aggregating,

filtering unnecessary (manual or automatic), completing and

fixing

� Analysis of Web data: running the recommendation/mining

algorithm to build the model (association rule, model-based

collaborative filtering, statistics over the product attributes,

…)

� Decision-Making and Recommendation: using the results

of the previous steps and the session-related information

provided by the user.

[Adomavicius and Tuzhilin, 2005a]

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Personalization Process

[Adomavicius and Tuzhilin, 2005a]

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Virtuous Cycle

� Personalization can deliver value to the stakeholders if

the “virtuous cycle” is achieved

� If we do not adjust the personalization strategy, or is

we do not measure its impact, then we have

depersonalization

� Consumers get frustrated and stop using the

system

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Processes

� We should make a distinction between three major parallel processes

1. Process followed by the decision makers (DM). Steps (conscious or not) the human DM may take

� E.g.:data collection, problem restructuring, evaluation of alternatives, choice

� Features: flexible, simple, suboptimal, dynamic,

2. Business processes. Processes involving many DMs aimed at achieving a shared business goal

� Features: rigid/flexible, hierarchically structured, goal oriented, explicit/implicit

3. Processes internal to the System. Mostly passive, with respect to the human DM ones, black boxes, very rigid.

� In addition processes could even be the alternative options for the Decision Maker (e.g. select the best process to ship X from A to B)

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Recommender Systems and IR

� Recommender system research has taken techniques from IR (e.g. content-based filtering)

� IR deals with large repositories of unstructured content about a large variety of topics – RSs focus on smaller content repositories on a single topic

� Personalization in IR (personalized search engines) did not received much interests (e.g. personalized google)

� IR deals with “locating relevant content” – the user should be able to evaluate the relevance of the retrieved set

� RS deals with “differentiating relevant content” – the user has not enough knowledge to evaluate relevance

– E.g. imagine to select a camera with google and with dpreview.com

� IR and RS supports different stages of the information search/discovery process

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IR and RS

Query User Model

Results

Feedback

Information Retrieval

Q1 Q2 … Qn

Queries or visited pages

Q* User Model

Results

Feedback

Content-Based Filtering

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RS and User Modeling

� User modeling is central in RS

� No single definition of UM – it depends on the

recommendation technique

– E.g. a set of product ratings (CF) or a vector of terms

weights (CB), or a case (KB)

� User model must depend on the product to be recommended

� Heterogeneity of User Models makes it difficult to design a

generic recommender system that can recommend any kind

of products

� Generic user modeling (ontologies) [Heckernann et al., 2005],

mediation of user models, ubiquitous user models [Berkovsky,

2006] are steps towards a general recommender system.

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RS and Scalability

� Scalability poses a serious concern for RS

� Collaborative filtering has first identified this problem

and studied various solutions (dimensionality reduction,

caching, clustering, ecc.)

� Less studied how content-based filtering or knowledge

based systems can scale: in the number of users and in

the number and type of products

� Example: CBR recommender system with million of

cases (products) – similarity retrieval is typically slow

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Scalability

� Many techniques rely on an extensive description of the items and users (memory based) – all of them suffer from a scalability problem

� Techniques that can be used to cope with that:

– Instance selection

– Feature selection

– Clustering and partitioning

– Abstraction

– P2P architectures

– Parallel architectures

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Individual vs. Collaborative

� Individual: personalization can be based on the knowledge of the target user only. For instance:

– Content based filtering

– Utility

– Some knowledge based systems

– Item-to-item CF (when item similarity is not computed with the ratings)

� Collaborative: personalization is based on knowledge of a population of users (in addition to the target user)

– Collaborative filtering

– Demographic

– Some hybrid knowledge-based systems

[Anand & Mobasher, 2005]

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Individual vs. Collaborative

� Individual

– ☺ easy to explain, easy to maintain, scale up with users,

can fit the user specific preferences, can be implemented

at client side

– � lack of serendipity, require specific knowledge of the

user and the product, not applicable for ephemeral users

� Collaborative

– ☺ serendipity, may not use specific domain knowledge,

applicable in C2C scenarios

– � requires lot of data about the users, privacy concerns,

not easy to explain, more suited to simpler products.

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Reactive vs. Proactive

� Reactive approaches return personalized recommendations in response to a user request and by means of a dialogue

– Examples: critiquing, conversational cbr, utility, IR inspired methods, knowledge-based

� Proactive approaches learn the UM (possibly in a reactive way) and at recommendation time not necessarily require the user to provide input

– Examples: item-item and user-user collaborative filtering, content based (e.g. mail filtering)

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Reactive vs. Proactive

� Reactive

– ☺ context dependent, user involvement, flexibility, mixed

initiative, general application, mixed strategies

– � interaction cost, prone to interaction errors, knowledge required, design complexity

� Proactive

– ☺ low interaction cost, can recommend when they know

they can, suited for mobile contexts

– � context independent, lack of transparency (process), no exploitation of user reaction to recommendations

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Users vs. Items Information

� RS vary in the information they use to generate

recommendations

– Item related information: content descriptions of

items and general domain/product knowledge (e.g.

ontology)

– User related information: past preference ratings,

user online behavior, user preferences, user

characteristics

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Users vs. Items Information

� RS mostly based on item information are “individual” and those based on users information are “collaborative” – hence there are similar plus and minus

� A RS uniquely based on item information cannot provide a “personalized” return – it is more a IR system

� There is always a balance between the two, depending on:

– The recommendation process

– The product item characteristics – complexity and value of the product

– The level of personalization that is required (not always is needed)

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Memory based vs Model Based

� The personalization process consists of an offline and

online stage

� In the offline stage: collection and processing of data

pertaining to user interests and learning of the User

Model

� Memory based (lazy) approaches do not generalize

the data – they collect and store them

� Model based approaches build a system model that is

radically different from the mere list of collected data

� Model based approaches scale up better.

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Client Side vs Server Side

� Most of the recommender store user and item

information at the server side

� Multiple recommendation experiences of the same

users on different servers (on the same or similar

products) are not exploited

� Client side approaches could address the problem of

recommending multiple type of products and would

pose lighter concerns for privacy

� Not much research on client side recommendation –

PocketLens implements CF on a P2P architecture – each

peer maintains his ratings [Miller et al., 2004]

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New User Problem

� Personalization systems can work only if some information about the target user is available

� No User Model – No personalization

� If the required user model data are not available then try to arrive to them with a shortcut

– Web of trust to identify “similar users” in a CF system [Massa & Avesani, 2004]

– Mining publications of a user and its similar users help in defining the topics that are interesting for a target user [Middleton et al., 2004]

� The output is always a sort of similarity function!!!

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New Item Problem

� Not much a problem for content-based filtering: it is

the main advantage of content-based filtering

� Critical problem for user-user and item-item

collaborative filtering

� Solution: exploit similar items

– Example: NutKing/Dietorecs ranking based on

double similarity – an implicit rating for a similar

item is considered to be assigned to the target item

[Ricci et al., 2003]

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More in General …

� Data sparseness can be addressed in a number of way

– Using similar items/users

– Clustering item/users

– Reducing the dimensionality – filter and wrapper

methods for feature selection

– Using other source of relational knowledge about

items/users: web of trust, ontologies, co-occurrence

relations

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Privacy

� Recommender systems are based on user information

� There are laws that impose restrictions on the usage

and distribution of information about people

� RS must cope with these limitations: e.g. distributed

recommender systems exchanging user profiles could

be impossible for legal reasons!

� RS must be developed in such a way to limit the

possibility that an attacker could learn personal data on

some users

� There is the need to develop techniques that limit the

number and type of personal data used in a RS.

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Privacy (2)

� Obfuscation: the (pseudo) random modification of a

subset of product ratings in a CF system has minor

impact on the accuracy [Berkovsky et al., 2006] [Polat

et al., 2003]

� Exchanging aggregate data rather than ratings: a

mediator collects personal data (from users) and reveal

only aggregate information: average ratings,

recommendations based on aggregate data [Berkovsky

et al., 2006] [Canny 2002]

– Users should be able to control what data to release

to the mediator.

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Recommendations Diversity

� Since recall is usually very low in RS, one objective is to provide at least a good diversity of the recommendations

� Mostly considered by content-based and knowledge-based methods

� Examples:

– Adaptive selection in comparison-based approaches [McGinty and Smyth, 2005]

– Adaptive selection of k-nearest products and greedy selection of diverse products in “seeking for inspiration”recommendation [Ricci et al., 2005]

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Recommendation and User Context

� All RS should adapt to the “context” of search of the user but some methods cannot cope with that easily (e.g. CF)

� It depends on the definition of “context” but in practice this includes

– “Short term” preferences (“tomorrow I want …”)

– Information related to the specific space-time position of the user

– Motivations of the search (“a present to my wife”)

– Circumstances (“I’ve some time to spend here”)

– Emotions (“I feel adventure”)

– Availability of data

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Economic and Social Context

Personalization

User

Knowledge, experience, budget, travel party, cognitive capabilities, motivations (security, variety, fun, etc.), age, language, disabilities

Device/Capabilities

Personal computer, PDA, smart phone, phone – payment, movies, broadcast, instant messaging, email, position, ecc.

Context • pre-travel vs. during travel

• on-the-net, on-the-move, on-the-tour

• traveling, wandering and visiting

• environment: in the hotel, train, car, airplane, at the conference

• Time/Space coordinates

• Business vs FunNeed• Buy a complete travel package,

• choose a restaurant,

• collect information on a location,

• find the route,

• find the transportation mean

• communicate with other travelers

System behavior and content adaptation

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Integration of Long term and Short Term UM

� If both short term (ephemeral) and long term (persistent) elements of the UM should be considered how to integrate them?

� Prefer short term: in [Nguyen et Ricci, 2004] short term preferences are always preferred if these are known (explicit input), otherwise use long term, or infer UM using a collaborative principle

� Switch: In NewDude (recommending news) [Billsus & Pazzani, 2000]

– if a target news is too similar to a news in the short term then it is considered as a repetition of something already read

– If enough similar to the stories in the short term then these are used to predict if the rating

– Otherwise the long term user profile is used to predict the rating of the target news

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Trust

� Trust is a quality of a recommendation or of a user participating in the social process of the recommendations

� [Swearingen & Sinha, 2001] define Trust-Generating Recommendations as “good” recommendations that the user had already experienced and enjoyed - increase users’confidence in the RS

� [Herlocker et al., 1999] Define a trustful users as those that have a large number of overlapping ratings with the target user (this make the similarity more trustful).

� [Massa and Avesani, 2004] in a CF system, allow users to rate users (in addition to products). When a recommendation must be computed for a target user trust/ratings of neighbor users are exploited for selecting those to exploit in the CF prediction.

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Robusteness

� The recommender should be robust against attacks

aiming at modifying the system such that it will

recommend a product more often than others (shilling)

� Some algorithms may be more robust than others

� Content based methods are not influenced at all by

false ratings

� Item-to-item CF is less influenced than user-to-user CF

[Lam and Riedl, 2004]

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Evaluation of RS

� There are many criteria for evaluating RS

– User satisfaction/usability

– User effort (e.g. time or rec. cycles required)

– Accuracy of the prediction

– Success of the prediction (the product is bought after the recommendation)

– Coverage (recall)

– Confidence in the recommendation (trust)

– Understandability of the recommendation

– Degree of novelty brought by the recommendation (serendipity)

– Transparency

– Quantity

– Diversity

– Risk minimization

– Cost effective (the cheapest product having the required features)

– Robustness of the method (e.g. against an attack)

– Scalability

– Adaptivity to changes in the data (users and items)

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Evaluation of RS (1)

� Current research has focused on “accuracy” – designing new methods to better predict existing ratings data

� The evaluation criteria must depend on the product and on the user task

– Example: recommend a book for a) teaching a course or for b) holiday reading

– A) trust, coverage, understandability, quantity

– B) serendipity, accuracy

� In the majority of situations the user cannot immediately evaluate the recommendation – this can be done only after the product has been experienced (e.g. a travel)

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Evaluation of RS (2)

� The recommendation influences (persuade) the user

� A recommender can be better in persuading the user of

the goodness of the recommendation rather than in

guessing its accuracy

– Example: suggest a camera with features that the

user will never exploit

– Before the recommendation process the user would

not consider this as a good recommendation

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Evaluation of RS (3)

� Having a set of good performance metric is not sufficient

� We must be able to understand – track back – the causes of a particular system behavior and fix the right system component

� Ex. The recommendation algorithm may be OK but the system has not enough content for that technique (e.g. sorting a catalogue of 20 products)

� Ex. The algorithm is ok but the presentation is poor in usability (very common!)

� Exploiting the evaluation is difficult because the “factors” influencing the system ultimate performance are many and cannot be decoupled

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Challenges

� Generic user models (multiple products and tasks)

� Generic recommender systems (multiple products and tasks)

� Distributed recommender system (users and products data are distributed)

� Portable recommender systems (user data stored at user side)

� (user) Configurable recommender systems

� Multi strategy – adapted to the user

� Privacy protecting RS

� Context dependent RS

� Emotional and values aware RS

� Trust and recommendations

� Persuasion technologies

� Easily deployable RS

� Group recommendations

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Challenges (2)

� Interactive Recommendations – sequential decision making

� Hybrid recommendation technologies

� Consumer Behavior and Recommender Systems

� Complex Products recommendations

� Mobile Recommendations

� Business Models for Recommender Systems

� High risk and value recommender systems

� Recommendation and negotiation

� Recommendation and information search

� Recommendation and configuration

� Listening customers

� Recommender systems and ontologies

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Summing up

� At the beginning – user recommendations

(ratings/evaluations) are used to build new

recommendations – collaborative or social filtering

– The recommender system is a machine that burns

recommendations to build new recommendations

� The expansion – many new methods are introduced

(content-based, hybrid, clustering, …) – the aim is to

tackle information overload and improve the behavior

of CF methods (considering context and product

descriptions)

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Summing up (2)

� Decision support – recommender systems are tools for

helping users to take decision (what product to buy or what

news to read)

– The gain in “utility” (personalized) without and with

recommendation is the metric;

– Information search and processing cannot be separated

from the RS research;

– The recommendation process becomes an important factor

– Conversational systems are introduced

– More adaptive and flexible conversations should be

supported

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Adaptive Advisory Systems - AAS

� A AAS is adaptive to the search and decision context and supports cooperative (collaborative) human-computer and human-human interactions

� AAS has beliefs about user needs or wants. Beliefs are built andupdated by interacting with the user, either querying or observing her

� AAS maintains an explicit model of the task process, i.e., the joint system’s and user states, the supporting actions and user actions, the deterministic and stochastic rules governing the state transitions

� AAS supports the user information search and decision making process by maintaining a dialogue with the user, where both parties can take, in turn, active (query, ask, suggest, etc.) and passive (reply, look at, compare, etc.) roles

� AAS can learn user preferences, and adapt its behavior in order to optimize objective measures of its performance, such as minimization of the interaction length, maximization of the number of successfully completed tasks.

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Thanks lot for your patience

And I hope that something is now spinning in your brains ….