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).