A Personalized Assistant Framework for Service Recommendation
Bibliographic record
Abstract
The popularity of service-oriented computing makes more and more services available on the Web. Users make use of these services to achieve their personal goals, such as purchasing movie tickets on-line and booking flights. Existing research has proposed various techniques to assist users to select services for achieving user goals. Typically, user choice of services change under different contexts. However, these approaches cannot recommend the desired services based on the changes of user contexts, and are not able to learn from user service selection history. In this paper, we provide an intellectually cognitive personalized assistant framework to achieve user goals. In particular, considering user contexts and historical service selection, our framework interacts with users by asking relevant and necessary questions, and help users navigate through sets of services to identify the desired services. We have designed and developed a prototype as a proof of concept. We perform a case study to evaluate the effectiveness of our framework. On average, our framework, utilizing the learning-to-rank algorithm, namely AdaRank, improves the nine baseline approaches by 12.02%-31.52% in helping users find the desired services. Our user study results show that our framework is helpful in achieving user goals and useful in saving users' time in finding their personalized services faster.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".