Mobile User Profile Acquisition through Network Observables and Explicit User Queries
Bibliographic record
Abstract
This paper describes a novel approach for gathering profile information about mobile phone users. The focus is on information that can be used to enhance targeting of advertisements. (The ads might be delivered into the mobile phones, or to other devices such as the user's IPTV.) Unlike previous approaches, we use a two-tiered approach for learning end-user habits and preferences. In this approach the first tier involves statistical learning from network observable data (in the current paper, primarily logs of cell towers visited), and the second tier involves explicit queries to the user (in the current paper, to ask, e.g., what kinds of activities the user does in a given region that he frequents). The user might be willing to answer occasional queries of this sort through offers of service discounts, or to be able to receive more relevant ads. The paper focuses on two key aspects of our approach, which correspond to how the two tiers are instantiated in the current version of the prototype system that we have developed at Bell Labs. The first concerns the statistical techniques used to determine information about regions visited, along with the frequency of visits, typical durations, and typical visit times. These techniques were developed based on a training set consisting of logs of 6 users with mobile devices over a period of several months. The techniques address issues that arise when a given small region is serviced by multiple cell towers (in which case oscillations between cell towers can be confused with movement between regions). The second key aspect concerns optimizing the order in which queries are presented to users, in a context where different query answers have different value for the advertising process. (The values of answers might be influenced by the mix of advertising campaigns from which ads are to be matched against users.) Optimization is NP-complete in a relatively general context. We develop a polynomial time algorithm which yields optimal sequences for the case where the family of queries to be asked satisfies a tree-based property. This is extended to create a heuristic polynomial time algorithm for the general case.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".