You Today, Better Tomorrow: Envisioning the Role of Conversation in Recommender Systems of the Future
Bibliographic record
Abstract
Recommender systems could evolve from traditional models of recommendation that largely harness data on past interactions to predict what a user might want in a given moment, towards systems that also support and nurture user self-actualization. This shift could guide users in exploring and fulfilling the needs of their future potential selves, untethered from their past and current identities. In this provocation, we suggest that interactive conversational recommendation is a suitable means to rouse this vision. Conversational recommendation is capable of eliciting real-time and layered preferences, and can enable systems to take on a more proactive role in dialoguing with users about their aspirational needs—particularly in helping users navigate the intricacies that often surround these needs. We also examine the potential challenges associated with the realization of such recommender systems—for instance, the complexities in transitioning from past-based patterns of personalization to those that accommodate present-oriented and future-oriented personalization, and the preservation of user agency whilst broadening the scope of roles recommender systems can play. Overall, this paper advocates for a necessary progression in recommender systems, one propelled by conversational recommendation, towards designs that not only avail present-day user needs, but also actively stimulate pathways toward the actualization of their potential and aspirational future selves.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".