Information Filtering / Personalization

1. Information Filtering /Personalization Luz M. Quiroga Stimulate 2005 IF-Personalization / Luz. M. Quiroga

2. Information Filtering (IF) /Personalization • What do we understand for IF? • How different is IF from IR • Why do we might need it? • What personalization means to you? • Do you make use of it? For what purpose? IF-Personalization / Luz M. Quiroga

3. Blocking, delivering Profiles, Information needs, user modeling Organizing, Searching, finding, discovering Web design, Usability, personas Database, web, e-mail, distribution lists, blogs, community of practice Recommenders, alert, agents Privacy, ethics, trust From class feedback IF / personalizationissues / related concept IF-Personalization / Luz M. Quiroga

4. Information Filtering: variants • SDI (selective dissemination of information) • Current awareness • Alert • Routing • Customization • Recommenders • Personalization IF-Personalization / Luz M. Quiroga

5. Main concepts in IF • Information Filtering .vs. Information Retrieval (definition) • Profiles • User models • Agents IF-Personalization / Luz M. Quiroga

6. IF v.s. IR. Definitions of IF • “a field of study designed for creating a systematic approach to extracting information that a particular person finds important from a larger stream of information” (Canavese 1994, p.2). • “tools … which try to filter out irrelevant material” (Khan & Card 1997, p.305) • a process of selecting things from a larger set of possibilities, then presenting them in a prioritized order (Malone et al. 1987). IF-Personalization / Luz M. Quiroga

7. Defining Information Filtering Belkin & Croft, 1992. “IF and IR: two sides of the same coin” • Typical characteristics of the IF process • Document set: Dynamic • Information need: Stable, long term, specified in a profile • Profile: Highly personalized • Selection process: Delegated • Filtering: “the process of determining which profiles have a high probability of being satisfied by particular object from the incoming stream” IF-Personalization / Luz M. Quiroga

8. Retrieval System Model (Douglas Oard) User Query Formulation Detection Selection Index Examination Indexing Docs Delivery IF-Personalization / Luz M. Quiroga

9. IF System Model Profile acquisition User profile Information need (long term) Detection Selection (delegated:agent) Index Examination Delivery Indexing Docs(dynamic) IF-Personalization / Luz M. Quiroga

10. Why do we need IF? • Internet growth is exponential: MIDS (Matrix Information and Directory Services) home page: http://www.mids.org/ • One of the impacts of Internet is that any person with access to the Internet can become an author and a publisher. As a consequence, the quality of the information to be found in the Internet is extremely diverse and the quantity of information available is enormous (Lynch 1997) Information overload IF-Personalization / Luz M. Quiroga

11. Information overload • With the explosion of information, the major concerns are not availability but obtaining the right information. Information that is highly important for one individual has no meaning for many others • “at least 99% of available data is of no interest to at least 99% of the users (Bowman et al. 1994, p. 106). IF-Personalization / Luz M. Quiroga

12. The need for IF: History • 1945: Vannevar Bush / Memex “... There is a new profession of trial blazers, those who find delight in the task of establishing useful trails through the enormous mass of the common record..” • 1958, Luhn: Selective Dissemination of Information • 1965: Ted Nelson / Xanadu / Hypertext • ... Professionals who would compete to create better trails, which would attract more users and royalties ..... IF-Personalization / Luz M. Quiroga

13. The need for IF: History • 1969: Hollis & Hollis: “Personalizing Information processes” • the amount of information was doubling every seven to ten years • 1982, Denning (ACM president / Filtering e-mail) • 1987: Malone: Social filtering (collaboration - annotation in documents - groupware) IF-Personalization / Luz M. Quiroga

14. The need for IF: History Information Filtering / Users profiles / agents • Need a system that selectively weed out the irrelevant information based on users preferences (user profile) • The system will act on behalf of the user and will deliver selected, prioritized information (active, agent) IF-Personalization / Luz M. Quiroga

15. Profiles • User characteristics; user preferences • Profiles are the basis for the performance of IF systems: • “the construction of accurate profiles is a key task -- the system’s success will depend to a large extent on the ability of the learned profile to represent the user’s actual interest” (Balabanovic & Shonan 1997, p.68) • building a “good” profile is still the central obstacle to achieving reasonable performances in IF systems • Need: evaluation of IF (profiles) • Fidel (corporations’ employees) • Quiroga (consumer health information systems) IF-Personalization / Luz M. Quiroga

16. User modeling • In order to build a good system in which a person and a machine cooperate to perform a task it is important to take into account some significant characteristics of people (Elaine Rich, 1983) • User models are personal characteristics of the user that the system maintains (Chris Borgman) • A profile can be thought as a user model. IF-Personalization / Luz M. Quiroga

17. Profiles, IF and User modeling All information filtering models and systems are based on modeling the user and presenting his information needs in the form of a profile [1] A conceptual framework for the design of IF systems come from two established lines of research: IR & User Modeling [2] [1] Shapira, Peretz & Hanani. Dept. of Industrial Engineering, Ben Gurion University; Dept. of IS, Bar-Ilan University [2] Oard & Marchionini. University of Maryland IF-Personalization / Luz M. Quiroga

18. Agents • Software programs that implement user delegation[1] • A personal assistant who is collaborating with the user in the same work environment; information filtering is one of the many applications an agent can assist [2] • Mental agents / Society of agents. Each mental agent can only do small process; joining these agents in societies leads to true intelligence [3][1] Jansen James. Phd Candidate Texas University, Computer Sc. US Academy Military. Research: combination of agents & search engines[2] Maes, Patty. MIT Media Lab. Research AI[3] Minsky, Marvin. The Society of minds, 1986 IF-Personalization / Luz M. Quiroga

19. Types of user models (Rich) Depending on: • The user being modeled • Individual • Canonical (stereotype; group) • Acquisition model • Explicit (stated) • Implicit (inferred) IF-Personalization / Luz M. Quiroga

20. Individual / Canonical user models (Elaine Rich) • Individual: Each user with one interface; appropriate to his/her need; emphasis in individual differences • Canonical [stereotype, group]]: The user is part of a group; interface for the group; emphasis in what the group has in common • Shared knowledge; community of practices • Collaborative filtering • Influencing the design of web sites for e-commerce IF-Personalization / Luz M. Quiroga

21. Individual / Canonical user models (Elaine Rich) • GRUNDY: an example of a canonical type of user model • A case study in the use of sterotypes • Grundy recommends novels that people might like to read • Stereotypes contain facets that relate to people’s taste in books • Grundy learns from user feedback: have they read it / liked it (reinforcement); if not, why? • Experiments showed that Grundy does significantly better with the user model than without it • It is a good start toward the construction of individual models IF-Personalization / Luz M. Quiroga

22. Explicit: [stated]. The model is built by the system based on explicit information provided by the user Implicit: [inferred]. The model is built by the system by mean of a learning process based on: User feedback (inferred from responses) User behavior (inferred from action) -> AGENTS Issues to consider: How to capture “user pre-Knowledge” ? User effort User control (acceptability, understanding) Explicit / Implicit user models (Rich) IF-Personalization / Luz M. Quiroga

23. ASIS: Closing keynote presentations. Plenary debate; the future of IR, IF • ASIS2001 • James Hendler: chief scientist of the Information System Office at the Defense Advanced Research Agency. He has Joint appointments in the Computer Science, the Electrical Engineering Department and the Advanced computer studies at University of Maryland, College Park • Ben Schneiderman: Professor in the Department of Computer Science at the University of Maryland, College Park. Founder of the Human-Computer Interaction laboratory; fellow of ACM; he received the ACM CHI lifetime Award in 2001 • ASIST 2004 • Tim Berners-Lee : inventor of the WWW; currently director of the W3C (World Wide Web Consortium) IF-Personalization / Luz M. Quiroga

24. ASIS: Closing keynote presentations. Plenary debate; the future of IR, IF • James Hendler (asist 2001) • Solution: AUTONOMOUS AGENTS: when we need information, one way to find it is to talk to an expert; both engage in a conversation; the expert learns about our needs, constrains and preference; the expert presents options; we decide. • Ben Schneiderman (asist 2001) • Solution: Good Interfaces; with autonomous agents we loose control; we can not trust agents; who has the power: the agent or the user? • Tim Bernster (asist 2004) • The semantic web; ontological representation of knowledge (metadata) • Critics: any system that requires metadata is meant to fail IF-Personalization / Luz M. Quiroga

25. Some other user modeling techniques • Social and collective profiles • Collaborative filtering • Social data mining • Filtering and communities of practices IF-Personalization / Luz M. Quiroga

26. Social Profiles • Ardissono & Goy (1999) • SETA: A recommender system for electronic shops • Based on Stereotypes • Profiles include “beneficiaries models”: user models for each third person for whom the shipper is selecting goods IF-Personalization / Luz M. Quiroga

27. Social profiles • Petrelli et al (1999) • Personalized guides to museums • Based on stereotypes • Study suggest including “family profiles” besides the individualized museum guide IF-Personalization / Luz M. Quiroga

28. Collaborative profiles • A process where the system gives suggestions based on information gleaned from members of a community or peer group. • Example: Amazon • People who (bought, read) X also (bought, read) Y IF-Personalization / Luz M. Quiroga

29. Social data mining • Blogs • Community of practices / knowledge sharing IF-Personalization / Luz M. Quiroga

30. Web usability / Personas / User models for web design • Sources: • Personas: Setting the Stage for Building Usable Information SitesBy Alison J. Headhttp://www.infotoday.com/online/jul03/head.shtml • Alan Cooper, The Inmates Are Running the Asylum: Why High-Tech Products Drive Us Crazy and How to Restore the Sanity, Indianapolis: Sams, 1999 IF-Personalization / Luz M. Quiroga

31. Web usability / Personas / User models for web design • Personas are hypothetical archetypes; imaginary • Personas are defined by their goals (detailed) • Developed through a series of ethnographic interviews with real and potential users. • Demographic (quantitative) data, such as age, education, and job title. (similar to marketing segmentation) • More important: to collect qualitative data (persona) • Interfaces are built to satisfy personas' needs and goals. IF-Personalization / Luz M. Quiroga

32. Personalization and web designWeb usability / Personas • Alan Cooper original idea: using a fictitious user with a set of goals to guide and focus the design of a product. • “His original idea was turned out into a rigorous form of user model, based on behavior patterns that emerge from ethnographic research.” • “A set of personas represents the key behaviors, attitudes, skill levels, goals, and workflows of real people we interview and observe, which we then use along with scenarios to guide the product's functionality and design.” • “The method has matured to the point that anyone trained in it should be able to get the same personas from the same data.” IF-Personalization / Luz M. Quiroga

33. Personalization - environments where is being used • Databases • Newsgroups, discussion lists • Personal Information Management (desktop files, E-mail, bookmarks, etc.) • News: electronic journals • Search engines • Web sites • Business • e-commerce • e-health • e-etc. IF-Personalization / Luz M. Quiroga

34. LIS 678: IF & PersonalizationExample of Special topics (previous semesters) • Privacy and personalization • E-commerce and personalization • Mining usage data for web personalization • Machine learning and personalization • Adaptive web sites: learning from visitor access patterns • Children's information seeking for electronic resources • Users' criteria for relevance in IF systems • Patterns in the use of search engines • Satisfaction of information users • Individual differences in organizing, searching, retrieving and evaluating information • Information retrieval technologies for special users IF-Personalization / Luz M. Quiroga

35. LIS 678: IF & PersonalizationExample of Special topics (this semester) • Personal Ontologies • Personal Information Management • Social / Collaborative filtering (wikis, blogs, community of practice) • Desktop searching • Semantic Web: metadata, XML, RDF • Probabilistic IR / IF IF-Personalization / Luz M. Quiroga

36. LIS 678: IF & PersonalizationExample of projects (this semester) • Technology and literacy in developing countries (panel) • Business application of IF products • Personalized ranking • Semantic web and personalization IF-Personalization / Luz M. Quiroga

37. IF Independent studies • Alex Guilloux: usability study of bookmarking behaviour; how specificity level in the hierarchy of bookmarks affect relevance • Susan Lin: • Bookmarking software; specification for design • Bookmarking habits of reference librarians (Information Architecture class) • Steve Lum: Ontology mapping; bookmark mapping for collaborative filtering • Jennifer Cambell: Personalization and communities of practice (evaluation) IF-Personalization / Luz M. Quiroga

38. LIS 678: Projects • Evaluation, comparisonof IR / IF systems (e.g. search engines; recommenders, personalization features in digital libraries and portals) • Designing / running an IR/IF experiment (e.g. building a collaborative profile using a movie recommender; testing usability of a search interface; incorporating personalization in the design of a digital library) • Analysis / design / prototype of a IR/IF component (e.g. a ranking algorithm; building a prototype of a searching interface; designing personalized web sites) • Writing a paper: literature review, reaction paper on IR/IF/User modeling • Conducting research or development on IF - User modeling (e.g. using faceted classification schemes for personalized web-IR); using bookmarks as a source of profiles; visualization for personal information management; observing users' searching behavior - children, young adults, patients, students, members of a community) IF-Personalization / Luz M. Quiroga

39. Exercises • Use Sifter filtering system http://ella.slis.indiana.edu/~junzhang/demo.html • Use the information filtering agent at: http://www.ics.uci.edu/~pazzani/Publications/ - download several papers of interest and see what recommendations you get • Use the movielens system: http://movielens.umn.edu/ rate movies (you decide how many you need to rate to adjust your profile) and see what recommendations you get For all exercises discuss: • Content of the profile • Is the profile representing user interests? • To what extent do these systems allow the user control over their profile? IF-Personalization / Luz M. Quiroga

40. People / Resources • Douglas Oard IF page: http://www.ee.umd.edu/medlab/filter/ • SIFTER Projecthttp://sifter.indiana.edu/ IF-Personalization / Luz M. Quiroga

41. People interested in IF in UH • User modeling: Martha Crosby, David Chin • User – Information interaction: Diane Nahl • Filtering in corporations: Bob SW. • Profile acquisition and representation: Luz Quiroga IF-Personalization / Luz M. Quiroga

42. Comments • Comments, Questions? • Thanks! IF-Personalization / Luz M. Quiroga