1. 1 of 49 chris.staff@um.edu.m t CSA3212: Lecture 7 © 2005- Chris Staff University of Malta Dr. Christopher Staff Department of Intelligent Computer Systems University of Malta Lecture 7: Recommendation Techniques CSA3212: User-Adaptive Systems

2. 2 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff Aims and Objectives Global Reconnaissance Techniques PowerScout Watson HyperContext Recommender Systems User Modelling in IR User Modelling in Recommender Systems

3. 3 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff Readings recommender p36-soboroff.pdf SOTA Recommender systems Lit Review.pdf (Chapter 8 - ) recommender 0329_050103.pdf burke-umuai02.pdf

4. 4 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff What is Recommendation? Recommendations are suggestions It 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

5. 5 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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

6. 6 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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

7. 7 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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

8. 8 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff Global Reconnaissance 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 support budzik99watson.pdf

9. 9 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff Global Reconnaissance 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

10. 10 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff More examples... Recommender systems Content recommendation Collaborative recommendation

11. 11 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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 :-)

12. 12 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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? ???

13. 13 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff Recommender Systems: Collaborative Recommendation Usually, ratings-based feedback Users must indicate degree to which they like product, product is fit for purpose, etc The recommendation is based on the weighted average utility of the product...... of users with the same preferences! preferences may also include demographics

14. 14 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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

15. 15 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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

16. 16 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff Recommender Systems: Content-based Instead of using ratings, use product features Identify features using eg., kdd96_quest.pdf On what basis can products be compared? Genre, cost, dimensions, etc. Recommendations can be based on user- selected feature sets, or on prior interactions Latter works for frequent recommendations of similar product (e.g., movie) but not infrequent ones, e.g., camera purchase

17. 17 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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?

18. 18 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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

19. 19 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR and Recommender Systems User model is usually created and maintained for information retrieval and recommender systems

20. 20 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling In pure IR, user interaction is usually geared towards selecting relevant documents from a collection/repository

21. 21 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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...

22. 22 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR This part based heavily on www.scils.rutgers.edu/~belkin/um97oh/

23. 23 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR 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

24. 24 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR 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

25. 25 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR 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)

26. 26 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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

27. 27 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR In ad hoc IR user may need support to: Reformulate the query to get better results Provide 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 info system builds model of user’s interests

28. 28 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR ASK-based IR (Belkin et al, 1982) elicits and represents user’s Anomalous State of Knowledge rather than specific info need Associative network represents ASK Uses rules to compare ASK with document representations User ratings of features can auto update ASK

29. 29 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR Modelling user goals (Vickery, Vickery & Brooks, 1980s) to determine the comparison techniques to apply for different users uses direct elicitation + implication from user behaviour long term modelling of user preferences and “typical” info problems

30. 30 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR Models for identifying UM functions in IR Abstract analysis of IR task. To identify: goals of IR problems in achieving goals what’s necessary for other actors in the system to know of user to achieve goals/overcome problems  query as specification of modelling function

31. 31 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR IR interaction as dialogue what 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)

32. 32 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR Observing user behaviour in IR systems settings cognitive task analysis failure analysis thinking aloud, etc.  Stereotypical models of experience, expertise, search behaviours, “needs”

33. 33 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in IR 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

34. 34 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in Recommender Systems Recommender systems Content-based (very similar to IR) Collaborative Aim is to make recommendations based on what other, similar, users liked or did recommender 0329_050103.pdf

35. 35 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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  R where R is a totally ordered set (of, e.g., reals)

36. 36 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS 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?

37. 37 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS What information is retained about users? Demographic information Interaction history Ratings given to items

38. 38 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS Two main types of algorithm Memory-based Model-based

39. 39 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS Memory-based algorithm heuristics 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) where Ĉ is set of N users who are most similar to user c and who have rated item s

40. 40 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS 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

41. 41 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS Many ways of deriving user similarity measure Normally based on the set of items, S xy, that both users, x and y, have rated Two popular approaches Correlation-based Cosine-based

42. 42 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS Correlation-based approach where is the average rating given by user x

43. 43 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS Cosine-based approach 2 users x and y are treated as vectors in m- dimensional space, where m is the number of items in S xy

44. 44 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS Memory-based approaches need many ratings to work well Default voting improves rating prediction accuracy

45. 45 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS Model-based algorithm to measure user similarity uses 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).

46. 46 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff User Modelling in RS Breese et al proposed two alternative probabilistic models to estimate the probability expression Cluster model (Naive Bayesian) Users are clustered into groups Bayesian 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

47. 47 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff Collaborative System Shortcomings New user problem New item problem Sparsity Can initially be resolved using demographic data

48. 48 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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 interests In which case, the method of interaction is usually different, and recommendations are based on content

49. 49 of 49 chris.staff@cs.um.edu.mt University of Malta CSA3212: Lecture 7 © 2005- Chris Staff 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