Social Navigation Peter Brusilovsky School of Information Sciences University of Pittsburgh

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Presentation transcript:

Social Navigation Peter Brusilovsky School of Information Sciences University of Pittsburgh

You are in Japan and its lunchtime. There are many food places around, but you do not know how good they are or what these places are actually offering. All signs are in Japanese Social Navigation in Real World “…without knowing much, we joined the longest existing queue formed for a sushi restaurant. looking at faces of people (both young and old) filled with expectations despite the long wait in the cold weather, we were sure that the food would be worth every minute of waiting time. well, it was”. (A comment on Flickr image, used in Rosta Farzan’s Thesis)

Social Navigation in Real Life What would you do…? Walking by the cinema you feel like watching a movie, but none of the movies seems familiar You missed a lecture and want to do your readings. You have a textbook and 100 assigned pages to read, but do not know what was most important in the lecture and what can be skipped You are hiking along a trail to a famous waterfall. You reached an unmarked road split and you have no map

Social Navigation Natural tendency of people to follow each other Making use of “direct” and “indirect cues about the activities of others Following trails Footsteps in sand or snow Worn-out carpet Using dogears and annotations Giving direction or guidance Navigation that is conceptually understood as driven by the actions from one or more advice provider

What do you do?

Social Navigation vs. General Navigation Walking down a path in forest Walking down a road in a city Reading a sign at the airport to find the baggage claim Talking to a person at the airport help desk to find the baggage claim

The Lost Interaction History What is the difference between walking in a real world and browsing the Web? –Footprints –Worn-out carpet –People presence What is the difference between buying and borrowing a book? –Notes in the margins –Highlights & underlines –Dog-eared pages –Opens more easily to more used places

Social Navigation in Information Space Synchronous Communication in real time Asynchronous Using the Interaction of past users Direct Direct communication between people Indirect Relying on user presence and traces of user behavior Chats Forums Presence of other people History-enriched environments Direct Indirect SynchronousAsynchronous

Direct Asynchronous SN Asynchronous discussion forums Recommending information to friends and community Directly asking questions for getting information Sharing bookmarks with others

Umtella: Direct Asynchronous SN

EDUCO: Synchronous, Indirect SN

Amazon: Asynchronous, Indirect Compare with an Amazon review: “the remake of this movie is horrible, I recommend to watch the original version instead”

SN in Information Space:The History History-enriched environments –Edit Wear and Read Wear (1992) –Social navigation systems Footprints, Juggler, Kalas Collaborative filtering –Manual push and pull Tapestry, LN Recommender –Modern automatic CF recommender systems Social bookmarking –Collaborative tagging systems Social Search

Properties Proxemic Transparent space in that signs and structures can be easily understood Passive Allowing passive collection of history without interfering users’ tasks Rate/form of change Summarizing what has happened Degree of permeation Separating interaction history from the object Social “we all benefit from the experience, preferably someone else’s”

Kind of information What Searching for value Giving guidance Who Doing things with friends Doing things with people who are similar to me Establishing authority and authenticity Why Doing similar things Discovering similar goals Explanation and learning How Showing how to do things

Edit Wear and Read Wear (1992) The pioneer idea of asynchronous indirect social navigation Developed for collaborating writing and editing Indicated read/edited places in a large document

The Pioneers: Footprints Wexelblat & Maes, 1997 Allowing users to create history-rich objects Providing History-rich navigation in complex information space Contextualizing Web pages –Maps –Path view –Annotations –Signposts

Footprints: Maps Showing the traffic through a website Nodes Documents Links Transition between them Tracking transition from all possible sources Selecting a link Typing a URL Selecting a bookmark Externalization of users’ mental model Click to see the title Double click to open the document Popularity of documents is shown by shades of red Current open document in black

Footprints: Path View Lower level view What paths have been followed by other people Relevant to current open document Merging path with common starting points Matching the framework Social? Passive? Proxemic? Unpermeated? Thickness of the path represents the usage amount

Footprints: Annotations Showing what percentage of users have followed each link Link-centric social navigation approach

Footprints: Signposts Allowing users to enter feedback On pages On paths “go this way for software agents; go that way for artificial life” Viewing comments left by other users How we can classify this social navigation?

The Pioneers: Juggler Dieberger, 1998 Textual virtual environment (MOO) History-enriched environment Showing access-counter for rooms Recognizing URLs in the output of a communication tool Hiding it from user Popping out the page Integrating with social navigation Supporting interaction between teachers and students

The Pioneers: Juggler Pointing out pages While talking to people By saying them Looking at people and object Associating URL with people, objects, and locations Pointing out button Pointing out (sharing) the current page

Ideas for Social Navigation on WWW Awareness of presence of other users –Discussion of an article –Location attracting large crowds of users Relevant objects –Links visited by similar users –Items appreciated by similar users Recency –How long ago the page was created/visited Attitude –What other users did/thought about an item

Example: CoWeb

More Advanced Projects Which users’ actions are taken into account for personalization? –All users –Coherent, like-minded group of users Group-level social navigation –KnowledgeSea II –CourseAgent –CoFIND

Knowledge Sea II Assisting students finding educational resources on the web Social Navigation Traffic based –Using intensity of colors to present footprints of other students Distinguishing the most and the least visited pages Annotation based –Using visual cues to present students’ annotation activity magnitude of group annotation activity presence of learners annotation magnitude of individual annotation activity

Knowledge Sea: Map

Knowledge Sea: Cells & Pages

CourseAgent Adaptive community based course recommendation system Provides personalized access to course information Provides social recommendation about courses Recommendation in the form of in-context adaptive annotation Visual cues Expected course workload Expected relevance to students’ career goals Course Schedule Course Catalog

Course Schedule

Course Catalog

CoFIND Collaborative bookmark database Self-Organized Database of Resources Combination of usage and explicit ratings causes the system to dynamically and continuously reorganize its resources.

Stigmergy Communication via the environment Nature Ant trails leave a trail of pheremones when find food and return to the nest The trail gets stronger, attracting more ants CoFIND Successful topic groups, topics, qualities and resources tend to grow more successful, influencing patterns of behavior for all users of the system

Challenges Concept drift Snowball effects Bootstrapping

Concept Drift Old history information becomes less relevant History decay different for a very popular and a less popular information Shift of Interest

Snowball effect Just one visit before the current visit can turn the page into ‘hot’ The page could be useful or useless Next users follow the same path Snowball gets bigger and bigger

Bootstrapping Social navigation works with many users What if there are very few users? How to match a new user against already populated system? How to encourage users to leave their trails (commenting, …)? How to make the new information visible in already populated system?

Discussion