Recommender Systems in TEL

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    Recommender Systems in TEL - Presentation Transcript

    1. Recommender Systems in TEL Nikos Manouselis Greek Research & Technology Network (GRNET) nikosm@ieee.org
    2. about me
      • Computer Engineer
      • MSc on Operational Research
      • PhD from Informatics Lab of an Agricultural University
      • working on services for agricultural & rural communities
        • learning repositories
        • social information retrieval
        • Organic.Edunet e Content plus
    3. (promised)aim of this lecture
      • introduce recommender systems
      • discuss how they relate to TEL
      • identify open research issues
    4. (actual)aim of this lecture
      • share some concerns about TEL and recommender systems
    5. structure
      • tale of 3 friends
      • tasks
      • modeling & techniques
      • evaluation
      • wrap up
    6. intro: tale of 3 friends
    7. which movie?
    8. lets ask some friend
      • “ Guys, heard about the last Batman movie… should I watch it?”
      “ You will definitely like it” “ Maybe not, the scenario is too weak”
    9. lets ask some friend
      • “ Wait – did you like the previous one?”
    10. … so, which movie?
      • taking advantage of knowledge or experience from people in the social circle or network
        • e.g. colleagues, friends, peers
      • need to answer several questions
        • how to identify like-minded people?
        • on which dimensions?
        • for which types of items?
        • does context matter?
    11. recommender systems
    12.  
    13.  
      • 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 (1/2)
    14. definition (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
    15. 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
    16. wait a second
      • is TEL like a super market??
    17. large number of options
    18. tasks for recommender systems
    19. tasks usually supported
      • annotation in context
      • find good items
      • find all good items
      • receive sequence of items
      • (+some less important ones)
      • Herlocker et al., “Evaluating Collaborative Filtering Recommender Systems” ACM Transactions on Information Systems, 22(1), 5-53, 2004.
    20. 1. annotation in context
      • integrated in existing working environment to provide additional support or information, e.g.
        • predicted usefulness of an item that the user is currently viewing
        • links within a Web page that the user is recommended to follow
    21. annotation in context
      • Screenshot/example
    22. 2. find good items
      • suggesting specific item(s) to a user
        • characterized as core recommendation task, since occurring in most systems
        • e.g. presenting a ranked list of recommended items
    23. find good items
      • Screenshot/example
    24. 3. find all good items
      • user wants to identify all items that might be interesting
        • when its important not to overlook any potentially relevant case
        • e.g. medical or legal cases
    25. find all good items
    26. 4. sequence of items
      • sequence of related items is recommended to the user
        • e.g. entertainment applications such as TV or radio programs
    27. sequence of items
    28. and what about TEL?
      • informal reminder:
        • technology enhanced learning is generally dealing with the ways ICT can be used to support learning , teaching , and competence development
      • [http://cordis.europa.eu/fp7/ict/telearn-digicult/telearn_en.html]
    29. break2think
      • bring yourself in one typical learning situation that occurs very often to YOU
    30. break2think
      • imagine that some magic TEL system is there to support you
        • it could make some great suggestions about something to you
      • name one learning task where a recommender system would be useful
    31. modeling & techniques
    32. 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)
    33. example: content-based
    34. example: collaborative filtering
    35. generally speaking: some user
      • has a profile with some user characteristics, e.g.
        • past ratings [collaborative filtering]
        • keywords describing past selections [content-based recommendation]
    36. generally speaking: some items
      • are represented using some dimensions, e.g.
        • satisfaction over one (or more) criteria [collaborative filtering]
        • item attributes/features [content-based recommendation]
    37. generally speaking: a mechanism
      • is taking advantage of the user profile and the item representations
        • it provides personalised recommendations of items to users
    38. rings some bell?
        • for TEL, this sounds so…
        • adaptive educational hypermedia systems ( AEHS )
    39. a generic architecture [Karampiperis & Sampson, 2005]
    40. an example [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
    41. recommend in TEL based on what?
      • on learner models/profiles
        • e.g. learning styles, competence gaps
        • … other ideas?
      • on item characteristics
        • e.g. interactivity, granularity, accessibility
        • … other ideas?
    42. evaluation
    43. 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]
    44. typical results means that a prediction could be 4,6 stars instead of 4 or 5 … does this really matter in TEL?
    45. other issues
      • live experiments vs. offline analyses
      • synthesized vs. natural data sets
        • properties of data sets
        • existing data sets
    46. 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
    47. 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
    48. 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)]
    49. 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 c ommunity-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 ]
    50. another look at it
      • e.g. using Kirckpatrick’s model on evaluating training programs
        • reaction of student - what they thought and felt about the training
        • learning - the resulting increase in knowledge or capability
        • behaviour - extent of behaviour and capability improvement and implementation/application
        • results - the effects on the business or environment resulting from the trainee's performance
    51. what else could be evaluated?
      • when deploying a recommender system in a TEL setting
      • … what could we evaluate and how to measure it?
    52. wrap up & directions
    53. basic conclusion
      • assuming an information overload problem in TEL
        • recommender systems are good
        • need to think out of the box
        • connect with existing research
        • focus on TEL particularities
        • explore alternative uses
        • integrate with existing theories
    54. interesting (?) issues
      • recommendation of peers
      • criteria for expressing learner satisfaction (no more 5-stars)
      • study actual usage/acceptance
      • assess performance/learning improvement
      • … implement, deploy, pilot!
    55. but do they exist??
      • http://www.oerrecommender.org
    56. interested in more?
      • Journal of Digital Information (JoDI)
        • Special Issue on Social Information Retrieval for Technology-Enhanced Learning, 10(2), 2009
      • Workshop on Social Information Retrieval for Technology Enhanced Learning (SIRTEL)
        • SIRTEL 2007 (http://ceur-ws.org/Vol-307)
        • SIRTEL 2008 (http://ceur-ws.org/Vol-382)
        • SIRTEL 2009 (http://celstec.org/sirtel)
          • co-located with ICWL’09, Aachen, Germany, August 21 st - deadline: 12/6
    57. thank you! questions? ideas?

    + telss09telss09, 5 months ago

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