SOCIAL INFLUENCE IN RECOMMENDATION AGENTS: CREATING SYNERGIES BETWEEN MULTIPLE RECOMMENDATION SOURCES FOR ONLINE PURCHASES
Bibliographic record
Abstract
With the increased popularity of online social networks, friends become an available recommendation source for decisions that are made on the Internet, such as online purchases. There is substantial benefit in integrating different recommendation sources into one recommendation system so that more information and indeed more relevant information can be provided to the user. However, there is also the burden on the user of having to cope with the broader scope of and sometimes differing advice provided. This paper focuses on the issue of potential cognitive dissonance between the user?s own preferences, social influencer?s (e.g., friend?s) recommendations, and advice from a recommendation agent (RA). It provides a model of how different recommendation system designs can lead to different magnitudes of dissonance and when. It also discusses the role of the user?s product knowledge on influencing the extent of and reaction with dissonance. This paper contributes to the designing of recommendation systems which can create synergies between different recommendation sources to best assist the user.
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 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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".