Recommender Systems Recommender Systems in TELin TEL

Nikos ManouselisNikos ManouselisGreek Research & Technology Network (GRNET)Greek Research & Technology Network (GRNET)

nikosm@ieee.org nikosm@ieee.org

about meabout me•Computer EngineerComputer Engineer•MSc on Operational ResearchMSc on Operational Research•PhD from Informatics Lab of an Agricultural PhD from Informatics Lab of an Agricultural

UniversityUniversity•working on services for agricultural & rural working on services for agricultural & rural

communitiescommunities– learning repositorieslearning repositories– social information retrievalsocial information retrieval– Organic.Edunet Organic.Edunet eeContentContentplusplus

(promised)aim of this (promised)aim of this lecturelecture

• introduce recommender introduce recommender systemssystems

•discuss how they relate to TELdiscuss how they relate to TEL• identify open research issuesidentify open research issues

(actual)aim of this (actual)aim of this lecturelecture

•share some concerns about TEL share some concerns about TEL and recommender systemsand recommender systems

structurestructure

•tale of 3 friendstale of 3 friends•taskstasks•modeling & techniquesmodeling & techniques•evaluationevaluation•wrap upwrap up

intro: tale of 3 friendsintro: tale of 3 friends

which movie?which movie?

lets ask some friendlets ask some friend

““Guys, heard about the last Batman Guys, heard about the last Batman movie… should I watch it?”movie… should I watch it?”

“You will definitely

like it”

“Maybe not, the scenario is

too weak”

lets ask some friendlets ask some friend

““Wait – did you like the previous one?”Wait – did you like the previous one?”

……so, which movie?so, which movie?

• taking advantage of knowledge or experience taking advantage of knowledge or experience from people in the social circle or networkfrom people in the social circle or network– e.g. colleagues, friends, peerse.g. colleagues, friends, peers

• need to answer several questions need to answer several questions – how to identify like-minded people?how to identify like-minded people?– on which dimensions?on which dimensions?– for which types of items? for which types of items? – does context matter?does context matter?– ……

recommender recommender systemssystems

• using the opinions of a community of users– to help individuals in that community to

identify more effectively content of interest

– from a potentially overwhelming set of choices

Resnick P. & Varian H.R., “Recommender Systems”, Communications of the ACM, 40(3),1997

definition definition (1/2)(1/2)

definition definition (2/2)(2/2)

• any system that – produces individualized

recommendations as output – or has the effect of guiding the user in a

personalized way to interesting or useful objects in a large space of possible options

Burke R. “Hybrid Recommender Systems: Survey and Experiments”, User Modeling & User-Adapted Interaction, 12, 331-370, 2002

why do we need them?why do we need them?• A trip to a local supermarket [F. Ricci]:

– 85 different varieties and brands of crackers– 285 varieties of cookies– 165 varieties of “juice drinks”– 75 iced teas– 275 varieties of cereal– 120 different pasta sauces– 80 different pain relievers– 40 options for toothpaste– 95 varieties of snacks (chips, pretzels, etc.)– 61 varieties of sun tan oil and sunblock– 360 types of shampoo, conditioner, gel, and mousse.– 90 different cold remedies and decongestants.– 230 soups, including 29 different chicken soups– 175 different salad dressings

wait a secondwait a second

is TEL like a super is TEL like a super market??market??

large number of optionslarge number of options

tasks for tasks for recommender recommender

systemssystems

tasks usually supportedtasks usually supported

1.1. annotation in contextannotation in context2.2. find good itemsfind good items3.3. find find all all good itemsgood items4.4. receive sequence of itemsreceive sequence of items

(+some less important ones)(+some less important ones)

Herlocker et al., “Evaluating Collaborative Filtering Recommender Systems” ACM Transactions on Information Systems, 22(1), 5-53, 2004.

1. annotation in context1. annotation in context

• integrated in existing working integrated in existing working environment to provide additional environment to provide additional support or information, e.g.support or information, e.g.– predicted usefulness of an item that predicted usefulness of an item that

the user is currently viewingthe user is currently viewing– links within a Web page that the user links within a Web page that the user

is recommended to followis recommended to follow

annotation in contextannotation in context

• Screenshot/exampleScreenshot/example

2. find good items2. find good items

• suggesting specific item(s) to a suggesting specific item(s) to a useruser– characterized as core characterized as core

recommendation task, since recommendation task, since occurring in most systemsoccurring in most systems

– e.g. presenting a ranked list of e.g. presenting a ranked list of recommended itemsrecommended items

find good itemsfind good items

• Screenshot/exampleScreenshot/example

3. find all good items3. find all good items

• user wants to identify user wants to identify all all items items that might be interestingthat might be interesting– when its important not to when its important not to

overlook any potentially relevant overlook any potentially relevant casecase

– e.g. medical or legal casese.g. medical or legal cases

find all good itemsfind all good items

4. sequence of items4. sequence of items

• sequence of related items is sequence of related items is recommended to the userrecommended to the user– e.g. entertainment applications e.g. entertainment applications

such as TV or radio programssuch as TV or radio programs

sequence of itemssequence of items

and what about TEL?and what about TEL?

• informal reminder:informal reminder: – technology enhanced learningtechnology enhanced learning

is generally dealing with the ways is generally dealing with the ways ICTICT can be used to support can be used to support learninglearning, , teachingteaching, and , and competence developmentcompetence development

[http://cordis.europa.eu/fp7/ict/telearn-digicult/telearn_en.html][http://cordis.europa.eu/fp7/ict/telearn-digicult/telearn_en.html]

break2thinkbreak2think

• bring yourself in one typical bring yourself in one typical learning situationlearning situation that that occurs very often to occurs very often to YOUYOU

break2thinkbreak2think

• imagine that some imagine that some magicmagic TEL TEL system is there to support yousystem is there to support you

– it could make some great it could make some great suggestionssuggestions about about somethingsomething to to youyou

• name name one learning task one learning task where a recommender system where a recommender system would be would be usefuluseful

modeling & techniquesmodeling & techniques

typical classification

• content-based: information needs of user and characteristics of items are represented in some (usually textual) form

• collaborative filtering: user is recommended items that people with similar tastes and preferences liked

• hybrid: methods that combine content-based and collaborative methods

…other categorizations also exist (Burke, 2002)

example: content-based

example: collaborative filtering

generally speaking: some generally speaking: some useruser

• has a profile with some user has a profile with some user characteristics, e.g.characteristics, e.g.– past ratings past ratings [collaborative filtering][collaborative filtering]

– keywords describing past keywords describing past selections selections [content-based [content-based recommendation] recommendation]

generally speaking: some generally speaking: some itemsitems

• are represented using some are represented using some dimensions, e.g.dimensions, e.g.– satisfaction over one (or satisfaction over one (or

more) criteria more) criteria [collaborative [collaborative filtering]filtering]

– item attributes/features item attributes/features [content-based recommendation][content-based recommendation]

generally speaking: a generally speaking: a mechanismmechanism

• is taking advantage of the is taking advantage of the user user profileprofile and the and the item item representationsrepresentations

– it provides personalised it provides personalised recommendations of items to recommendations of items to usersusers

rings some bell?

for TEL, this sounds so…for TEL, this sounds so…

adaptive educational adaptive educational hypermedia systemshypermedia systems ((AEHSAEHS))

a generic architecturea generic architecture

[Karampiperis & Sampson, 2005][Karampiperis & Sampson, 2005]

an examplean example

[Karampiperis & Sampson, 2005][Karampiperis & Sampson, 2005]

enhanced version of [Hanani et al., "Information Filtering: Overview of Issues, Research and Systems", User Modeling and User-Adapted Interaction, 11, 2001]

classification/analysis

recommend in TEL based on recommend in TEL based on what?what?

• on learner models/profileson learner models/profiles– e.g. learning styles, competence e.g. learning styles, competence

gapsgaps– ……other ideas?other ideas?

• on item characteristicson item characteristics– e.g. interactivity, granularity, e.g. interactivity, granularity,

accessibilityaccessibility– ……other ideas?other ideas?

evaluationevaluation

evaluating recommendation

• currently based on performance “how good are your algorithms?”

• e.g. – how accurate are they in predictions?– for how many unknown items can they

produce a prediction?

…mainly information retrieval evaluation approaches

[Herlocker et al., “Evaluating Collaborative Filtering Recommender Systems” ACM Transactions on Information Systems, 22(1), 5-53, 2004]

typical results

MAE per # of neighbors

0.55000

0.60000

0.65000

0.70000

0.75000

0.80000

0.85000

0.90000

0.95000

1.00000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

# of neighbors

MA

E

means that a prediction could be 4,6 stars instead of 4 or 5 … does this really matter in TEL?

other issues

•live experiments vs. offline analyses

•synthesized vs. natural data sets– properties of data sets– existing data sets

metrics (popular)

• accuracy– predictive accuracy (MAE)– classification accuracy

• Precision and Recall– probability that a selected item is relevant– probability that a relevant item will be selected

• ad hoc– Rank Accuracy Metrics– Prediction-Rating Correlation

• coverage– percentage of items for which prediction is

possible

metrics (not popular)

• novelty • serendipity• confidence• user evaluation

– explicit (ask) vs. implicit (observe)– laboratory studies vs. field studies– outcome vs. process– short-term vs. long-term

evaluation in TEL recommenders

• few systems actually evaluated– even fewer actually tried with users

• recent analysis of 15 TEL recommender systems:– half of the systems (8/15) still at

design or prototyping stage– only 5 systems evaluated through

trials with human users

[N.Manouselis, H.Drachsler, R.Vuorikari, H.Hummel, R.Koper, “Recommender Systems in Technology Enhanced Learning”, Handbook of Recommender Systems (under review)]

example: Altered Vista

• evaluate the effectiveness and usefulness– system usability and performance– predictive accuracy of recommender engine– extent to which reviewing Web resources within

a community of users supports and promotes collaborative and community-building activities

– extent to which critical review of Web resources leads to improvements in user’s information literacy skills

[Walker et al., “Collaborative Information Filtering: a review and an educational application”, International Journal of Artificial Intelligence in Education 14, 2004]

another look at it

• e.g. using Kirckpatrick’s model on evaluating training programsa. reaction of student - what they thought

and felt about the trainingb. learning - the resulting increase in

knowledge or capabilityc. behaviour - extent of behaviour and

capability improvement and implementation/application

d. results - the effects on the business or environment resulting from the trainee's performance

what else could be what else could be evaluated?evaluated?

• when deploying a when deploying a recommender system in a TEL recommender system in a TEL settingsetting

……what could we evaluate and what could we evaluate and how to measure it?how to measure it?

wrap up & directionswrap up & directions

basic conclusionbasic conclusion

• assuming an assuming an information information overload overload problem in TELproblem in TEL– recommender systems are recommender systems are goodgood– need to think need to think out of the boxout of the box– connect with connect with existing researchexisting research– focus on TEL focus on TEL particularitiesparticularities– exploreexplore alternative alternative uses uses – integrate with integrate with existing theoriesexisting theories

interesting (?) issuesinteresting (?) issues

• recommendation of peersrecommendation of peers• criteria for expressing learner criteria for expressing learner

satisfaction satisfaction (no more 5-stars)(no more 5-stars)

• study actual usage/acceptancestudy actual usage/acceptance• assess performance/learning assess performance/learning

improvementimprovement

……implement, deploy, pilot!implement, deploy, pilot!

but do they exist??but do they exist??

http://www.oerrecommender.orghttp://www.oerrecommender.org

interested in more?interested in more?

• Journal of Digital Information (JoDI)Journal of Digital Information (JoDI)– Special Issue on Social Information Retrieval Special Issue on Social Information Retrieval

for Technology-Enhanced Learning, 10(2), 2009for Technology-Enhanced Learning, 10(2), 2009• Workshop on Social Information Workshop on Social Information

Retrieval for Technology Enhanced Retrieval for Technology Enhanced Learning (SIRTEL)Learning (SIRTEL)

– SIRTEL 2007 (http://ceur-ws.org/Vol-307) SIRTEL 2007 (http://ceur-ws.org/Vol-307) – SIRTEL 2008 (http://ceur-ws.org/Vol-382)SIRTEL 2008 (http://ceur-ws.org/Vol-382)– SIRTEL 2009 (http://celstec.org/sirtel)SIRTEL 2009 (http://celstec.org/sirtel)

• co-located with ICWL’09, Aachen, Germany, August co-located with ICWL’09, Aachen, Germany, August 2121stst - deadline: 12/6 - deadline: 12/6

thank you!thank you!

questions? ideas?questions? ideas?