On Interest and Expertise

Inadequate representation of user interest was, ironically, a failing common to many of the innovation-support applications surveyed in the previous chapter. If the interest-activated approach we have determined to use is to be successful, an effective mechanism must be found by which the system can identify, evaluate and compare users interests. One way to achieve this is to simply allow users to specify their interests by hand, selecting combinations of topics from a common taxonomy maintained in the system. This is undoubtedly the most straightforward approach, though it has been observed that individuals tend only to explicitly declare their peripheral interests, omitting those that relate to their day-to-day work, with the result that true experts in a subject are never identifiable to the system [Lindgren03].

Several techniques have been developed that purport to model an individual s interests automatically from an aspect of their existing computer use. Some of these techniques have too high a degree of inherent latency to be of use in an innovation-led environment; we can discount, for example, those that rely on monitoring readership of formally published documents or bookstore purchasing trends. More appropriate are techniques that infer interest from personal notes or

exemplified by the hebb application [Carter03] outlined in Chapter 3, analysis of email has become the focus of attempts to achieve responsive automated interest recognition.

Schwartz and Wood pioneered the identification of interest groups from email archives [Schwartz92]. They developed an algorithm to identify clusters of tightly connected individuals from their email communication patterns; examining weeks of mail server logs to construct a social graph of who emailed who. The technique allows users to search for people by requesting a list of people whose interests are similar to several people known to have the interest in question . It requires there to be known, identifiable, distinguished person(s) for a particular interest that will form the root point(s) of the social graph analysis. The network revealed is somewhat akin to a Community of Practice; it is a clearly identifiable group of individuals who communicate regularly over a period of time. The resulting overview of the community could raise awareness between members on

opposite sides of its social graph. Messages could be addressed or events

advertised to people who re into the kind of stuff that John and Tom are doing .

This technique cannot put a name to that stuff though the only properties of an

email message considered in the analysis are the addresses listed in its from and to fields. Moreover, it cannot recognise lone individuals or small groups that have had no contact with a named root person, or locate those sharing a novel interest for which there are yet no distinguished persons .

More recently, Natural Language Processing (NLP) has been applied to mine the content of email messages for significant keyword clusters. Unfortunately, email presents a challenging environment for NLP because messages often are very short and assume a high degree of pre-existing common ground; email conversations are grounded in sufficient mutual understanding to allow very brief, sketchy and implicit references to succeed without posing significant problems in interpretation [Ducheneaut02]. Without access to this grounding, significant but niche or obliquely referenced concepts in the message

can be missed, or an unacceptably high number of spurious concepts identified (users of the hebb system reported that the word notes was interpreted as a common interest). If both the signal-to-noise ratio of interests identified and sensitivity of detection of genuine interests are to be improved, better access will be needed to the background and historical contexts (the assumed common ground) in which a communication takes place.

Nonetheless, it has been shown that by using NLP algorithms specifically tailored for this purpose, email message analysis can now successfully identify

long standing, widely communicated interests the kind that Lindgren suggests

are less likely to be explicitly declared to the system. McArthur and Bruza [McArthur03] employed a modification of the HAL (Hyperspace Analogue to Language) [Burgess98] semantic representational model as the basis of a system to identify the key players involved with a project or product in an organisation. Similar functionality has been deployed commercially within Tacit s ActiveNet

platform1:

Here's how Tacit's ActiveNet product works: Employees are told their e-mail or other files will be scanned to compile profiles of their interests. They may opt out if they don't want to participate. E-mail is then scoured, with frequently used phrases plucked out and categorized by topic. Over time, topics that fall off your radar are discarded and new ones added. 2

Email analysis may also offer a way to reveal related pair-wise collaborations that occur between individuals working in ostensibly unrelated fields; one can imagine, for example, that a seemingly crazy idea for colour-shifting clothing that emerges in a dialogue between a fashion writer and designer might never be purposely disclosed to a formal organisational awareness platform, yet if the pair

an academic project developing smart textiles, they may be inspired to collaborate to develop the idea further.

In our context, however, the derivation of users interests from their email chatter has a fundamental limitation: you can only send an email about an interest

and make that interest visible to the system if you are already aware of

someone who would share your interest, that you can send the email to. Individuals within an organisation who, unknown to each other, share a truly novel interest may work independently for months before deciding to speculatively publicise their efforts through email and finally becoming visible to the awareness system (or, worse, abandon the work without ever becoming aware of there being a potential collaborator). Clearly, a system to support innovation would, instead, aim to link such individuals at the earliest possible opportunity. In this regard, a useful development is the move towards integration of email (user to other, or user to others communication) and electronic note taking (i.e. user to self communication). Already, Pocket PC - based PDAs offer the facility to translate hand-written scribbles to machine-readable text and wirelessly transfer the resulting note to a desktop Outlook email client. Bellotti et al. point to deeper future integration of email and Personal Information Management (PIM) functionality with their ThinkDoc [Bellotti02] prototype, in which emails, notes, scanned documents and other such material can be woven together to form a rich tapestry-like work package that individuals can pass between themselves via the ThinkDoc server.

Compared with sketching on the back of an envelope or jotting notes in a pad, any form of computer note-taking seems clumsy; what the PDA gains in spontaneity and portability, it loses by its cramped display and fiddly user

interface. However, new Anoto3 digital pen products are blurring the distinction

between paper and screen, combining the intuitiveness of the spiral-bound

notebook with the machine-processibility and immediate availability of computer-

3

authored notes. Pens augmented with miniature cameras are used with special stationery, each page of which is pre-printed with a faint pattern of dots. These dots allow the pen to determine not only the unique identity of the sheet of paper on which it is resting, but also its exact position. Each ink mark made on the page is simultaneously recorded, electronically, within the pen. From there, the pen strokes are transferred wirelessly to the user s mobile phone, PDA or PC, where character recognition software turns them into ASCII text. Specially mapped dot patterns can be used to signify interactive interface elements called pidgets printed analogues of the digital text fields, check boxes and click buttons that are so familiar on the desktop. These could be used to, for example, mark an idea on the page private or public , or indicate that collaboration is invited from colleagues. Digital pen technology is in its infancy; the first generation of consumer products is expensive and a degree of user hand-holding is required to assist the software interpret the captured page. Nonetheless, it is very clear that, as digital pen and NLP technologies evolve, it will become possible to directly infer

users interests from their paper notes.

Given the limitations of current interest capture methodologies and the promise that emerging technologies hold, it would be unwise to lock our system into a particular automated technique. A decision has been made, therefore, to use interest templates that are intuitive enough that they can be human-managed in the short term, but have a sufficiently simple grammar that automated interest identification technologies can be retro-coupled to the system through the existing interest profile management interface, either to replace user management of interest profiles outright, or offer outline profiles that the user can refine or reject, without significant system modifications being required. In the mean time, we can aim to minimise the risk that core interests will not be represented by ensuring that interest declarations are treated as an intrinsic component of any statement of group membership.

So what of expertise? It is tempting to assume that expertise is a characteristic distinct from interest, somehow evaluable and modellable in its own right. It is true, of course, that one might be expert in a subject without necessarily finding it very interesting. Lindgren et al., though, drew some interesting conclusions on expertise and its relationship with interest in innovation-led organisations from their study of worker experiences within Volvo s IT research team [Lindgren03]. They identified that, in this environment, competence or expertise are not formally distinct from interest, but emerge organically from the application of interests in a professional context: pursuing a professional interest

in a corporate setting leads to competence within that area . Moreover, they

noted that while employees valued the ability to quickly identify an expert in a particular field, it was also considered that in an ever changing environment interest and commitment [can be] more important than formal competence ; they explain that it is the intrinsic motivation that comes from personal interests that sets the limits for the organization s future . The implication of this, then, is that the need is not for some separate representation of expertise, but for a means to identify formally applied interests.