TCENR: A Hybrid Neural Recommender for Location Based Social Networks
Bibliographic record
Abstract
Point-Of-Interests (POI) recommendation, an important application of location-based social networks (LSBN), has been extensively researched in recent years. This sub-field of recommender systems (RS) poses unique challenges due to high data sparsity and its relative complexity. An emerging technique is the use of deep neural networks to improve the performance of collaborative filtering (CF) based models. Recent works have successfully integrated such networks with external data, such as social networks, locations, categories and written reviews. In this paper, we propose a new method, Textual and Contextual Embedding-based Neural Recommender (TCENR). The suggested algorithm combines two types of neural networks to model the user-POI interactions based on implicit ratings, social networks, geographical locations and natural language reviews. Experiments on the Yelp dataset show that the proposed model is able to learn the complex interaction and enables improved recommendation performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".