Bibliographic record
Abstract
With the ever increasing popularity of Smartphone and reducing charges for device and data Mobile applications users are estimated to quadruple in the next three years. With the increase in the number of users Applications in the market are increasing by thousands every day. The Users are flooded with so much of choices that it is hard for them to find appropriate and Suitable apps. Recommender systems can aid the users in discovering new applications in a personalized manner. The purpose of this thesis is to investigate how to enhance the recommendations to a user in the process of discovering new mobile applications by better utilization of context data available through various sensors in a modern day Smartphone. The work of the thesis is divided into three phases where the aim of the first phase is to study related work and related systems to identify promising concepts and features. During the second phase, a prototype system is designed and implemented. The outcome and result of the first two phases is then evaluated and analyzed in the third and final phase. The prototype system integrates an existing mobile app recommender system to add the features of context awareness and context reasoning. The major draw of the thesis is to suitably define context and situations of interests and model them appropriately. Learning techniques are applied to learn these models using the context data generated by the user. The derived situation provides an added parameter in the process of information filtering in a personalized manner.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".