Proceedings of the International Workshop on Ubiquitous and Decentralized User Modeling:in conjunction with 11th International Conference on User Modeling, UM 2007. Corfu, Greece, June 26th, 2007
Bibliographic record
Abstract
Personalising or intelligently adapting an application to user's behaviour and context requires gathering and processing some information about the user.Data collected for user modelling may contain personal and sometimes confidential information.Hence, privacy issues raise with the use of personalisation and context-awareness technology.Is personal data collected and used appropriately, with full awareness and consent of the user?Is the user granted the adequate access rights to manage their personal data?Local privacy regulations have a strong impact on business operations and vary a lot from country to country.Another related issue is that of the security of collected user data; is it kept secured, uncorrupted, with appropriate tracking mechanisms and protection from unauthorized access?The ownership of that data can also be questioned: user profiles may describe how the user relates to their environment (social, digital or physical).As such, it contains both information about the user and their environment.Does such information fully belong to the user?Or to tuples of users involved in a given transaction or communication?Can it be owned by the content provider?By the network or service operator?Or by the device manufacturer?Last but not least, beyond legal and security matters, personalisation systems are only accepted when they are of good-enough quality and accuracy.In order to be accepted by users, their efficiency must exceed some quality threshold for users to accept them.By adopting personalisation systems, they not only put some of their privacy at risk but also their time and attention as they may receive irrelevant recommendations from poorly performing personalisation systems, as well as their money since the cost of the system is at the end somehow supported by end-users.When is a personalisation system good enough for being compliant with the users' expectations in terms of quality of service as well as in terms of cost?The economic viability of content-related business models may vastly rely on the accuracy allowed by the current state of the art in personalisation systems.For instance, how far can mobile advertisements targeting go in proposing business tradeoffs between the need of the advertisers to achieve high efficiency in advertising campaigns, the need for privacy of users and their hostility toward being distracted by irrelevant ads on their personal mobile device?Is there an appropriate price for letting users abandon their privacy and accept reading poorly targeted ads?Can advanced user modelling systems do anything better than that?All these issues will be presented and discussed through our talk planned during the UbiDeUM'2007 workshop (International Workshop on Ubiquitous and Decentralized User Modelling).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".