CSA402

Lecture 12

Problems that
Adaptive Hypertext Systems
attempt to solve

Problems

Lost in HyperSpace Syndrome

Characteristics
- User doesn't know where he/she is in relation to other (related) information in hyperspace
- User doesn't know how to access previously visited node
Causes
- Bad GUI design
- Poor organisation of information
- No links to landmark sites
- No/poor organisation of access history
- User unfamiliarity with content/ organisation

Cognitive Overload

Complexity of the search space

Browsing is a form of search
- Definition of the serendipitous browser "I'll know I've found what I want when I see it"
Advantage and disadvantage of hierarchical 'classification' systems (e.g., http://www.yahoo.com)
- User has to know where to find what she wants
- Document must be classified correctly
Lack of link semantics - no guarantee that in general hyperspaces a link is going to lead to greater detail
The more complex the search space, the harder for the user to construct an accurate conceptual model
The harder for the user to construct an accurate conceptual model, the greater the likelihood of the user getting disoriented
If the hyperspace is too simple, it probably won't cater for the needs of all possible users
If the hyperspace is too small, we can 'remember' where everything is
AHS has to try to create and support simplified conceptual models which precisely cater for the (individual) user's needs

Search-browsing

Searching and browsing are complementary tools for navigating through a hyperspace which does not provide a semantic representation of its contents
Normally, search is used to identify a node that is "close" to the required node, if not the required node itself
Browsing takes place so the user can understand the information content and make informed decisions about which link to follow
Sometimes also necessary, because user is unfamiliar with terminology, so needs to locate nodes which will enable user to specify more accurate query - compare to RF in IR systems
If user knows where information is, or if user knows how to get to information (from some known landmark), then search not needed
If user knows what is wanted, but doesn't know location of document, then search is required
Usually, search is performed at a different location from which user is browsing
Search-browsing would allow user to search while browsing, and the system may enable the user to follow a recommended path to the relevant node

Static hypertext structure

Hypertexts are usually static
Authors create hyperspace, and users traverse them
Hypertext generally cannot re-organise itself by learning from users
Users who want to create a more easily navigable hyperspace need to create it, possibly by replicating existing resources
Links cannot be modified to lead to more useful information, unless the user is the owner of the node
Compare to similar problem in IR systems - IR systems generally don't learn that query Q can yield different subsets of relevant documents D1 - Dn that have been selected by different users, but instead always yield the same results list
We know, from IR, that an infinite number of information needs can map to the same formal query. Therefore, unlikely that a results list R will satisfy all queries which map to Q

Inability to cater for different users with different needs and requirements

The problems described above lead to an inability to cater for different users with different needs and requirements
Next lecture, we will see how AHSs solve these problems

Different requirements of Hypertext and IR systems

Ideal AHS:
- As users browse through hyperspace, the AHS adapts the presentation of information and the organisation of hyperspace to the needs and requirements of individual users
A user model is implicitly required, but is usually missing in hypertext systems...
... what about IR systems?
Most hypertext systems do not directly support search. Why?
Most IR systems do not directly support browsing. Why?
In IR, relevant information is stored in single documents, or in passages (because that's what's indexed!)
In hypertext, relevant information can span multiple nodes
User interaction
- Hypertext: follow link - display selected document
- IR: submit query - display results list
Organisation
- Hypertext: network, in which arcs (links) reflect relevance
- IR: (at best) document clusters, in which clusters represent similarity. (At worst) no organisation - docs referred to in results list are relevant to query
Degree of relevance:
- IR: real number
- Hypertext: ??