Location Recommendation on Location Based Social Networks Utilizing Check-in Data and Location Category
Bibliographic record
Abstract
Location-based social networks (LBSNs) provide a platform for users to share their location information with each other. Location recommendation is the task of suggesting unvisited locations to the users. It aims to make satisfying recommendations of locations by utilizing the information such as users' visiting histories, user profiles and location profiles. This thesis investigates the utilization of check-in data and location category information for location recommendation on LBSNs. A distributed crawler is developed to collect a large amount of check-in data from Gowalla for the research. Then, three ways are used to utilize the check-in data, namely, binary utilization, FIF utilization, and probability utilization. According to different utilizations, different Collaborative Filtering recommenders are introduced to do location recommendation. Experiments are conducted to compare the performances of different recommenders using different check-in utilizations. Location category information is utilized for location recommendation by considering the temporal and spatial patterns. A user's periodic check-in behaviors at different location categories are represented as temporal curves. A temporal influence model is used to predict similar users' check-in behaviors based on temporal curves. A geographical influence model is proposed to filter out locations that are not of interest to the user. By integrating temporal influence and geographical influence a location recommendation algorithm called sPCLR is proposed to recommend locations to the users at a given time of the day. Experimental results show that the sPCLR algorithm performs better than three existing location recommendation algorithms.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".