Bibliographic record
Abstract
In recent times, collaborative filtering based Recommender Systems (RS) have become extremely popular. While research in recommender systems has mostly focused on improving the accuracy of recommendations, in this paper, we look at the "flip" side of a RS. That is, instead of improving existing recommender algorithms, we ask whether we can use an existing operational RS to launch a targeted marketing campaign. To this end, we propose a novel problem called RecMax that aims to select a set of "seed" users for a marketing campaign for a new product, such that if they endorse the product by providing relatively high ratings, the number of other users to whom the product is recommended by the underlying RS algorithm is maximum. We motivate RecMax with real world applications. We show that seeding can make a substantial difference, if done carefully. We prove that RecMax is not only NP-hard to solve optimally, it is NP-hard to even approximate within any reasonable factor. Given this hardness, we explore several natural heuristics on 3 real world datasets - Movielens, Yahoo! Music and Jester Joke and report our findings. We show that even though RecMax is hard to approximate, simple natural heuristics may provide impressive gains, for targeted marketing using RS.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.018 |
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".