Bibliographic record
Abstract
Context-aware recommendation systems (CARSs) leverage contextual information, e.g., time, location, or mood, to generate more personalized recommendations with high accuracy; however, existing CARSs fall short in: 1) handling the high sparsity of data; 2) designing scalable solutions in real time; and 3) providing more personalized solutions with the current limited static contexts. This article proposes a multi-CARS based on consensus clustering (MCARS-CC) to solve these challenges. The item-based contextual information is acquired using explicit static and inferred contexts by applying sentiment analysis to the users’ reviews. The proposed model is experimented using contextual prefiltering and postfiltering techniques applied to two benchmark datasets, Yelp and TripAdvisor. The model is evaluated using mean absolute error (MAE), root-mean-squared error (RMSE), response time, precision, recall, and F-measure. The experimental results show that the proposed MCARS-CC model outperforms other baseline techniques using the accuracy and error-based metrics. Incorporating hypergraph partitioning algorithm (HGPA) could improve the MAE and RMSE by 25.96% and 8.94% (Yelp), respectively. Also, HGPA led to an 18.47% and 15.94% improvement ratio in terms of MAE and RMSE (TripAdvisor), respectively.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".