Dr. Christopher Staff Department of Intelligent Computer Systems University of Malta

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CSA3212: Lecture 7© 2005- Chris Staff University of Malta

Dr. Christopher StaffDepartment of Intelligent Computer Systems

University of Malta

Lecture 7: Recommendation Techniques

CSA3212: User-Adaptive Systems

2 of [email protected] University of Malta

CSA3212: Lecture 7© 2005- Chris Staff

Aims and Objectives

Global Reconnaissance TechniquesPowerScoutWatsonHyperContext

Recommender SystemsUser Modelling in IRUser Modelling in Recommender Systems



Readings

recommender p36-soboroff.pdfSOTA Recommender systems Lit

Review.pdf (Chapter 8 - )recommender 0329_050103.pdfburke-umuai02.pdf



What is Recommendation?

Recommendations are suggestionsIt could be a suggestion to watch a

particular movie, or to buy a particular product, visit a restaurant (not fish!)

In hyperspace, this could be a suggestion to follow a path leading to a relevant document, or to visit a document directly



What is Recommendation?

If the recommendation is to do with guidance, then this is related to adaptive navigation

If the recommendation is based mainly on recommending products, then it is a recommender system

The two are, or can be, closely related, but the literature tends to deal with them separately



Examples...

Global Reconnaissance, Guidance, Personal Information Management Assistants...

As you browse a user model of your interests is automatically built

Paths are recommended, or other documents are collected for your perusal

Usually use IR systems to index, search for, and retrieve relevant documents



Global Reconnaissance

PowerScout (Lieberman, 2001)Automatically builds user model from recently

viewed pages, but based on user’s long-term interaction

Searches for relevant documents via 3rd party search engine

Organises results by “Concept”

Why-Surf-Alone.pdf




Watson (Budzik et al, 1998)Observes user interacting with several

applications to build model of user’s information goal

Anticipates that user is interested in documents similar to ones seen in recent past

Searches for documents (via 3rd party search engine) and presents list to user

Short-term user model, with long-term supportbudzik99watson.pdf




HyperContext (Staff, 2000)Uses Adaptive Information Discovery (AID)

techniques to find remote but relevant information

Short-term UM, with long-term UM support

HCTCh5.pdf



More examples...

Recommender systemsContent recommendationCollaborative recommendation



Recommender Systems

“What did you think about...?” “Did you like...?”

Make recommendation based on past experience

Real world examples: food critic, movie critic, book/novel critic, lecture course critic :-)



Recommender Systems

How do you know you can trust somebody’s recommendation?Because experience has taught you?Because critic is trusted source of info?Because a friend/expert likes movies/novels/

food you like????



Recommender Systems:Collaborative Recommendation

Usually, ratings-based feedbackUsers must indicate degree to which they

like product, product is fit for purpose, etcThe recommendation is based on the

weighted average utility of the product... ... of users with the same preferences!

preferences may also include demographics



Recommender Systems:Collaborative Recommendation

Do you want recommendations based on all users?

Or do you want recommendations from other people like you, with your tastes and preferences?

How can the system work out what you like/prefer/want?Comparing interactions (purchases, queries,

movies seen, etc.) and identifying trends



Recommender Systems:Cold-Start Problem

Collaborative recommender systems suffer from the cold start problem

How do you recommend a new product with no ratings?

How do you recommend to a new user?Content-based recommendation overcomes

some problems



Recommender Systems:Content-based

Instead of using ratings, use product features

Identify features using eg., kdd96_quest.pdfOn what basis can products be compared?

Genre, cost, dimensions, etc.Recommendations can be based on user-

selected feature sets, or on prior interactionsLatter works for frequent recommendations of

similar product (e.g., movie) but not infrequent ones, e.g., camera purchase



Recommender Systems:Cold-Start Problem Revisited

If user categorisation is automatic (i.e., System believes user U belongs to group G based on past interactions) then cold-start problem for new users

New products are ok, though, because they will be recommended based on feature similarity

If user drives feature selection, then is system user-adaptive?



Recommender Systems

Both collaborative and content-based recommendation utilise clustering techniques to identify patterns in users and/or products/items

Most common technique is the Vector Space Model

Other IR techniques also used



User Modelling in IR andRecommender Systems

User model is usually created and maintained for information retrieval and recommender systems



User Modelling

In pure IR, user interaction is usually geared towards selecting relevant documents from a collection/repository



User Modelling

Is there a user model, even a simple one, in this model of IR?

If there is, is there a point at which adaptation might be said to take place?

More next topic...



User Modelling in IR

This part based heavily on www.scils.rutgers.edu/~belkin/um97oh/




In early IR (before automation!) human mediators (e.g., librarians) construct queries on behalf of users See also, evaluation of boolean model (p289-

blair.pdf)Search intermediaries were still used in some

recent Web-based question-answering systems, e.g., Google Answers




As query specification languages became complex (1950s/60s) intermediaries needed to construct queries

It became useful in systems that performed Selective Dissemination of Information (SDI) to store representations of users’ long-term interests so that new information objects could be routed to them




Initially, user profiles were changed manually on basis of user’s evaluation of search results

Eventually, SDI could automatically modify profiles based on relevance judgements

This line of IR developed into information filtering (routing)



User Modelling in IR Ad hoc IR assumes that information need is just

one-time there is just one information seeking episode a single query is compared to a static document

collection If there is a subsequent query that is submitted by

the same user and that is related to a prior query, it is treated as a new episode




In ad hoc IR user may need support to:Reformulate the query to get better resultsProvide relevance feedback so that system can

modify the query (Rocchio, 1966)In “queryless” IR (Oddy, 1977) the user

need not specify the information need:user evaluates/rates features of retrieved infosystem builds model of user’s interests




ASK-based IR (Belkin et al, 1982)elicits and represents user’s Anomalous State of

Knowledge rather than specific info needAssociative network represents ASKUses rules to compare ASK with document

representationsUser ratings of features can auto update ASK




Modelling user goals (Vickery, Vickery & Brooks, 1980s)to determine the comparison techniques to

apply for different usersuses direct elicitation + implication from user

behaviourlong term modelling of user preferences and

“typical” info problems




Models for identifying UM functions in IRAbstract analysis of IR task. To identify:

goals of IRproblems in achieving goalswhat’s necessary for other actors in the system to

know of user to achieve goals/overcome problems

query as specification of modelling function




IR interaction as dialoguewhat is needed to experience effective conversation

(e.g., Grice’s rules of conversational implicature)how can these be modelling in an IR interaction?

models of understanding that each actor has of the other (“I believe that you believe...”, and see Kobsa’s BGP-MS)




Observing user behaviour in IR systems settingscognitive task analysisfailure analysisthinking aloud, etc.

Stereotypical models of experience, expertise, search behaviours, “needs”




Overall goal (not Belkin’s words!)Intelligent agents that can understand user

needs/goals/tasks by observing user behaviour and that can find, retrieve, or even accomplish, what the user had set out to do, without the user necessarily expressing his or her intentions



User Modelling in Recommender Systems

Recommender systems Content-based (very similar to IR)CollaborativeAim is to make recommendations

based on what other, similar, users liked or did

recommender 0329_050103.pdf



User Modelling in RS

In general, let C be the set of all users, and let S be the set of all recommendable items (CDs, books, movies, holidays, documents...)

Let u be a utility function which measures the usefulness of item s to user c

u:C x S Rwhere R is a totally ordered set (of, e.g., reals)




In RS, utility of an item to a user is usually represented as a rating, how much a particular user liked the item, but it can be any function

On what basis do we decide that two users are similar?




What information is retained about users?Demographic informationInteraction historyRatings given to items




Two main types of algorithmMemory-basedModel-based




Memory-based algorithmheuristics that make rating predictions based on

entire collection of previously rated items by users

Predict rating for user c on item s assuming user has not previously seen item (simplest)

€

rc,s =1

Nr ′ c ,s

c '∈ ˆ C

∑ where Ĉ is set of N users who aremost similar to user c and who have rateditem s




Problem with simplest algorithm...Doesn’t take into account similarity between

users, only similarity between prior ratings

sim(c,c’) is the similarity (distance measure)

between two users, k is a normalising function

€

rc,s = k sim(c, ′ c ) × r ′ c ,s

′ c ∈ ˆ C

∑

€

k =1/ sim(c, ′ c )′ c ∈ ˆ C

∑




Many ways of deriving user similarity measure

Normally based on the set of items, Sxy, that both users, x and y, have rated

Two popular approachesCorrelation-basedCosine-based




€

sim(x, y) =

(rx,s − r x )(ry,s − r y )s∈Sxy

∑

(rx,s − r x )2 (ry,s − r y )2

s∈xy

∑s∈Sxy

∑

€

r x

Correlation-based approach

where is the average rating given by user x




Cosine-based approach2 users x and y are treated as vectors in m-

dimensional space, where m is the number of items in Sxy

€

sim(x, y) = cos(r x ,

r y ) =

r x •

r y

r x 2 •

r y

2

=

rx,sry,ss∈Sxy

∑

rx,s2 ry,s

2

s∈xy

∑s∈Sxy

∑




Memory-based approaches need many ratings to work well

Default voting improves rating prediction accuracy




Model-based algorithm to measure user similarityuses collection of ratings to learn a model

which is then used to make rating predictions

the probability that user c will give a particular rating to item s given that user’s ratings of the previously rated items (Breese et al, 1998).€

rc,s = E(rc,s) = i × Pr(rc,s = i rc, ′ s , ′ s ∈Sc )i=0

n

∑




Breese et al proposed two alternative probabilistic models to estimate the probability expressionCluster model (Naive Bayesian)

Users are clustered into groupsBayesian networks

Each item is a node in the network, with states of each node representing possible rating values

Network and conditional probabilities are learned from data



Collaborative System Shortcomings

New user problemNew item problemSparsity

Can initially be resolved using demographic data



Conclusion

IR has users with both long- and short-term interests

RS has users with mainly long-term interests, although recommendations may be made to users with short-term interestsIn which case, the method of interaction is

usually different, and recommendations are based on content



Conclusion

In IR, an explicit user model is maintained for long-term support, but a query is a reasonable ad hoc model of the user’s interest

In RS, users need to be distinguished in the collaborative model, but not in the content model

Dr. Christopher Staff Department of Intelligent Computer Systems University of Malta

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