A Context-aware Recommendation System using smartphone sensors
Bibliographic record
Abstract
Nowadays, the ubiquity of mobile devices (such as smartphones and tablets) has encouraged the development of context-aware and personalized computation to filter the query results and provide recommendations based on different situations of a user. On the other hand, global positioning system (GPS) technology along with microelectromechanical system (MEMS) sensors enables location sensing on mobile devices. Context-aware computation can provide customized services in different contexts - where context is related to the user's location, activity and historical information. Our main focus in this paper, the Context-aware Recommendation System (Co-ARS), is one of the major applications that has been refined over the years due to the evolving geospatial technologies and data mining. The proposed Co-ARS application achieves the list of recommendations by utilizing the user's context information (such as location and preferred transportation mode), item's context information (such as restaurant ratings and types), and personalized preference information (based on individuals past behavior). In this paper, we have described the application of such a system in the City of Calgary using android smartphones. The implemented system used various data filtering and management techniques to provide beneficial and accurate recommendation results to the users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".