is202: information organization & retrieval recommender systems ray larson & warren sack...
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Recommender Systems
Ray Larson & Warren SackIS202: Information Organization and Retrieval
Fall 2001UC Berkeley, SIMS
lecture author: Warren Sack
Last Time
Guest Lecture:
Abbe Don on Information Architecture
(1) Guides
(2) We Make Memories
(3) don.com
Storytelling(narrative structures)
Information Architecture
Approach to User Interface Design
Interaction Design
MediaDesign
points of view
politics of information
scenarios
Slide by Abbe Don
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
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
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
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
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?
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
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)
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)
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
The Basic Idea
• The basic idea of collaborative filtering is people recommending items to one another. Terveen et al., 1997
Amazon.comHow 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/presentations/is296/ass1nonfiction.html
Social Networkscan be
Computer-based Networks (e.g., cross-indexed elements in a database)
Cf., Barry Wellman, “Computer Networks As Social Networks”, www.sciencemag.org,
Science, vol. 293, 14 September 2001
Resnick and Varian, 1997
Resnick and Varian, 1997
Resnick and Varian, 1997
GroupLens
Konstan, Miller, Maltz, Herlocker, Gordon, and Riedl
GroupLens
Konstan, Miller, Maltz, Herlocker, Gordon, and Riedl
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.
Fab Balabanovi and Shoham
Fab Balabanovi and Shoham
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)
Referral WebKautz, Selman and Shah
Referral WebKautz, Selman and Shah
Referral WebKautz, Selman and Shah
* Referral Web uses social networks extracted for public informationSources of the web.
• The current Referral Web system uses the co-occurrenceof 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)
PHOAKSTerveen, Hill, Amento, McDonald, Creter
PHOAKSTerveen, 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).
SiteseerRucker 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.
SiteseerRucker and Polanco
How do they work?An Example Algorithm
• Yezdezard Lashkari, Feature Guided Automated Collaborative Filtering, Masters Thesis, MIT Media Laboratory, 1995.
• Webhound
• Firefly
Webhound, Lashkari, 1995All automated collaborative filtering algorithms use the following steps to make a recommendation to a user:
Webhound, Lashkari, 1995
Webhound, Lashkari, 1995
Webhound, Lashkari, 1995
Webhound, Lashkari, 1995
Webhound, Lashkari, 1995
Webhound, Lashkari, 1995
From Items to PathsChalmers, Rodden & Brodbeck, 1998
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).