1. IS202: Information Organization & Retrieval Recommender Systems Ray Larson & Warren Sack IS202: Information Organization and Retrieval Fall 2001 UC Berkeley, SIMS lecture author: Warren Sack

2. IS202: Information Organization & Retrieval Last Time Guest Lecture: Abbe Don on Information Architecture (1) Guides (2) We Make Memories (3) don.com

3. IS202: Information Organization & Retrieval Storytelling (narrative structures) Information Architecture Approach to User Interface Design Interaction Design Media Design points of view politics of information scenarios Slide by Abbe Don

4. IS202: Information Organization & Retrieval Issues Understand the relationships between information architecture, interaction design and media design. Examine how organizational structures and politics affect information architecture and thereby the overall design process and the final user interface. Re-enforce the importance of needs assessment, user scenarios, user requirements, and clear product definitions, business goals, etc. Slide by Abbe Don

5. IS202: Information Organization & Retrieval Guides: Revised Characters –3 Content Characters in period dress Settler Woman Frontiersman Native American Always present in the interface: gestures revealed level of “interest” Recommended all media types based on “point of view” algorithm with weighted terms –Added “point of view” video stories for each character based on diaries and oral histories –1 System Character in contemporary dress Provided “context sensitive” help Recommended all media types based on emergent browsing pattern of the user Slide by Abbe Don

6. IS202: Information Organization & Retrieval Last Last Time Interfaces for Information Retrieval –What is HCI? –Interfaces for IR using the standard model of IR –Interfaces for IR using new models of IR and/or different models of interaction

7. IS202: Information Organization & Retrieval The standard interaction model for information access –(1) start with an information need –(2) select a system and collections to search on –(3) formulate a query –(4) send the query to the system –(5) receive the results –(6) scan, evaluate, and interpret the results –(7) stop, or –(8) reformulate the query and go to step 4

8. IS202: Information Organization & Retrieval HCI Interface questions using the standard model of IR Where does a user start? Faced with a large set of collections, how can a user choose one to begin with? How will a user formulate a query? How will a user scan, evaluate, and interpret the results? How can a user reformulate a query?

9. IS202: Information Organization & Retrieval Interface design: Is it always the HCI way or the highway? No, there are other ways to design interfaces, including using methods from –Art –Architecture –Sociology –Anthropology –Narrative theory –Geography

10. IS202: Information Organization & Retrieval Information Access: Is the standard IR model always the model? No, other models have been proposed and explored including –Berrypicking (Bates, 1989) –Sensemaking (Russell et al., 1993) –Orienteering (O’Day and Jeffries, 1993) –Intermediaries (Maglio and Barrett, 1996) –Social Navigation (Dourish and Chalmers, 1994) –Agents (e.g., Maes, 1992) –And don’t forget experiments like (Blair and Maron, 1985)

11. IS202: Information Organization & Retrieval Relevance is not just topic, but also… Recency Novelty Quality Availability Authority (Wang, ASIS 1997, 34, 162- 173) Utility (Cooper, JASIS 24: 87-100, 1973)

12. IS202: Information Organization & Retrieval Today Recommender systems (see also collaborative filtering, social filtering, social navigation) –Example systems: Amazon.com, GroupLens, Referral Web, Phoaks, GroupLens, Fab –How does it work? An Example Algorithm –Generalizations of the recommender systems idea; e.g., Social Navigation

13. IS202: Information Organization & Retrieval The Basic Idea The basic idea of collaborative filtering is people recommending items to one another. Terveen et al., 1997

14. IS202: Information Organization & Retrieval

16. Amazon.com How might one visualize Amazon’s “people who buy this book also buy…” feature? Examples from IS296a-2: Social Information Spaces www.sims.berkeley.edu/courses/is296a- 2/f01/assignments.html Vivien Petras’ visualization: www.sims.berkeley.edu/~vivienp/presentation s/is296/ass1nonfiction.html www.sims.berkeley.edu/~vivienp/presentation s/is296/ass1nonfiction.html

17. IS202: Information Organization & Retrieval Social Networks can be Computer-based Networks (e.g., cross-indexed elements in a database) Cf., Barry Wellman, “Computer Networks As Social Networks”, www.sciencemag.org,www.sciencemag.org Science, vol. 293, 14 September 2001

18. IS202: Information Organization & Retrieval Resnick and Varian, 1997

19. IS202: Information Organization & Retrieval Resnick and Varian, 1997

20. IS202: Information Organization & Retrieval Resnick and Varian, 1997

21. IS202: Information Organization & Retrieval GroupLens Konstan, Miller, Maltz, Herlocker, Gordon, and Riedl

22. IS202: Information Organization & Retrieval GroupLens Konstan, Miller, Maltz, Herlocker, Gordon, and Riedl

23. IS202: Information Organization & Retrieval GroupLens Konstan, Miller, Maltz, Herlocker, Gordon, and Riedl Usenet news is a domain with extremely high predictive utility. High predictive utility implies that any accurate prediction system will add significant value. So then, why do we need a collaborative filtering system? In general, users do not agree on which articles are desirable.

24. IS202: Information Organization & Retrieval Fab Balabanovi and Shoham

25. IS202: Information Organization & Retrieval Fab Balabanovi and Shoham

26. IS202: Information Organization & Retrieval Fab Balabanovi and Shoham To create a hybrid content-based, collaborative system, we [Balabanovi and Shoham] maintain user profiles based on content analysis, and directly compare these profiles to determine similar users for collaborative recommendation. (p. 68)

27. IS202: Information Organization & Retrieval Referral Web Kautz, Selman and Shah

28. IS202: Information Organization & Retrieval Referral Web Kautz, Selman and Shah

29. IS202: Information Organization & Retrieval Referral Web Kautz, Selman and Shah * Referral Web uses social networks extracted for public information Sources of the web. The current Referral Web system uses the co-occurrence of names in close proximity in any documents publicly available on the Web as evidence of social connection. Such sources include - Links found on home pages - Lists of co-authors in technical papers and citations of papers - Exchanges between individuals recorded in news archives - Organization charts (such as for university departments)

30. IS202: Information Organization & Retrieval PHOAKS Terveen, Hill, Amento, McDonald, Creter

31. IS202: Information Organization & Retrieval PHOAKS Terveen, Hill, Amento, McDonald, Creter PHOAKS works by automatically recognizing, tallying, and redistributing recommendations of Web resources mined from Usenet news messages. For a mention of a URL to count as a recommendation it must: (1)Not be posted to too many news groups (2)Not be part of a poster’s signature or signature file (3)Not be mentioned in a quotation from another message (4)Contain “word markers” that indicate that it is being Recommended (and not advertised or announced).

32. IS202: Information Organization & Retrieval Siteseer Rucker and Polanco Siteseer utilizes each user’s bookmarks as an implicit declaration of interest in the underlying content, and the user’s grouping behavior (such as placement of subjects in folders) as an indication of semantic coherency or relevant groupings between subjects. Siteseer looks at each user’s folders and bookmarks, and measures the degree of overlap (such as common URLs) of each folder with other people’s folders.

33. IS202: Information Organization & Retrieval Siteseer Rucker and Polanco

34. IS202: Information Organization & Retrieval How do they work? An Example Algorithm Yezdezard Lashkari, Feature Guided Automated Collaborative Filtering, Masters Thesis, MIT Media Laboratory, 1995. Webhound Firefly

35. IS202: Information Organization & Retrieval Webhound, Lashkari, 1995 All automated collaborative filtering algorithms use the following steps to make a recommendation to a user:

36. IS202: Information Organization & Retrieval Webhound, Lashkari, 1995

37. IS202: Information Organization & Retrieval Webhound, Lashkari, 1995

38. IS202: Information Organization & Retrieval Webhound, Lashkari, 1995

39. IS202: Information Organization & Retrieval Webhound, Lashkari, 1995

40. IS202: Information Organization & Retrieval Webhound, Lashkari, 1995

41. IS202: Information Organization & Retrieval Webhound, Lashkari, 1995

42. IS202: Information Organization & Retrieval From Items to Paths Chalmers, Rodden & Brodbeck, 1998

43. IS202: Information Organization & Retrieval Social Navigation From Recommender Systems to the more general issue of Social Navigation (Dourish and Chalmers, 1994) “The ideas of social navigation build on a more general concept that interacting with computers can be seen as “navigation” in information space. Whereas “traditional” HCI sees the person outside of the information space, separate from it, trying to bridge the gulfs between themselves and information, this alternative view of HCI as navigation within the space sees people as inhabiting and moving thrugh their information space. Just as we use social methods to find our way through geographical spaces, so we are interested in how social methods can be used in information spaces.” (Munro, Hook, Benyon, 1999).