Value-Ranked Feeds

• Value-ranked feeds are information ordering systems that incorporate explicit value criteria—such as fairness, diversity, and user priorities—instead of relying solely on engagement metrics.
• They use techniques like linear dot-product models and LLM-based value labeling to score and rank content transparently and accountably.
• Empirical studies indicate enhanced user satisfaction and control, though challenges like cognitive load and potential echo chambers remain.

A value-ranked feed is an information ordering mechanism where the sequence of items in a feed reflects explicit value criteria—either codified algorithmically or derived from user or societal input—rather than implicit engagement metrics alone. Such feeds appear in social media platforms, news aggregators, e-commerce, and content recommendation systems, where the aim is to optimize exposure, representativeness, fairness, and user alignment with well-defined values or utilities. The literature distinguishes value-ranking as a family of methods that either embed external value systems or allow users to articulate, measure, and control the values that underpin content ordering, in contrast to conventional algorithms that primarily maximize short-term engagement.

1. Foundations and Conceptual Models

Value-ranked feeds are rooted in the recognition that engagement-based algorithms are not value-neutral—they often privilege specific, sometimes individualistic, signals such as click-through, time-on-site, or virality, potentially neglecting broader or pluralistic values (Jahanbakhsh et al., 17 Sep 2025, Kolluri et al., 16 May 2025). Recent papers explicitly articulate this concern, arguing for a transition toward systems that surface values ranging from well-being and equity to productive discourse, fairness, informativeness, or community-specific priorities.

Formally, value is operationalized in various ways:

• As explicit utility values, e.g., predicted click value, conversion, or revenue (Dai et al., 2020);
• As the alignment with a vector of values (such as Schwartz’s 19 Basic Human Values, or a library of 78 pluralistic values) assigned post-content analysis via an LLM classifier (Jahanbakhsh et al., 17 Sep 2025, Kolluri et al., 16 May 2025);
• As user-weighted preferences that are linearly combined with post-level value intensities to yield a scalar ranking score: ${\rm Score}_i = \mathbf{w} \cdot \mathbf{v}_i$ where $\mathbf{w}$ is a vector of user (or institutional) value weights and $\mathbf{v}_i$ is the vector of values expressed in item $i$.

Value rankings can also be constructed to be fair, diverse, transparent, or explainable—criteria that may be interpreted as values in themselves (Yang et al., 2018, Chakraborty et al., 2018, Ghazimatin et al., 2019).

2. Technical Methods and Algorithms

A wide range of technical approaches to value-ranking have been proposed:

• Value Labeling and Alignment: Long-form LLMs score each post or candidate item against a value vector (either via textual definitions, value surveys, or prompt-based multi-label classification). These scores are then combined—typically as a weighted sum—to produce a “value alignment score” for ranking, supporting both pluralistic libraries (78 values in (Kolluri et al., 16 May 2025)) and theoretically-grounded taxonomies (19 values in (Jahanbakhsh et al., 17 Sep 2025)).
• User and Societal Controls: Users provide input through slider-based weight vectors, natural language specifications, or questionnaire responses (Malki et al., 13 Sep 2025, Jahanbakhsh et al., 17 Sep 2025, Kolluri et al., 16 May 2025). This input directly modulates the ranking vector.
• Algorithmic Implementation:
  • Linear Dot-Product Model: $\text{Score}_i = \mathbf{w} \cdot \mathbf{v}_i$ is used for sorting feed candidates (Jahanbakhsh et al., 17 Sep 2025, Kolluri et al., 16 May 2025).
  • Voting and Aggregation: The Single Transferable Vote (STV) system, with inferred or explicit ballots, is applied for aggregating crowd preferences and enforcing proportional representation (Chakraborty et al., 2018).
  • Greedy/Re-ranking Algorithms: Algorithms may be tailored to optimize feed utility or to balance accuracy, diversity, and value objectives; for example, BS-DPP re-ranking for joint accuracy-diversity (Xu et al., 2023), or greedy backward allocation for ad placement considering user attention decay (Zhang et al., 4 Feb 2025).
  • Transparency and Explainability: Nutritional label frameworks decompose feed scores and fairness/stability diagnostics as interactive widgets, promoting interpretability for auditors and end-users (Yang et al., 2018).
• Pipeline Architectures: Multi-stage pipelines are deployed, e.g., Planning–Sourcing–Curating–Ranking as in Bonsai (Malki et al., 13 Sep 2025), where user intent is parsed, candidate content is gathered, posts are filtered/curated and ranked according to value-aligned scoring.

3. Fairness, Diversity, and Value Pluralism

Several works engage directly with structural concerns:

• Fairness Enforcement: Approaches such as STV ensure “equality of voice,” proportional representation, and limit the influence of hyper-active minorities in top-K recommendations (Chakraborty et al., 2018). The concept of anti-plurality (minimizing the selection of items strongly disliked by the majority) is operationalized as a metric and as a constraint in STV-based selection.
• Diversity and Transparency: Nutritional label systems provide auditability for diversity (e.g., the distribution of department sizes or categories in top-K), stability (robustness to data/model perturbations), and fairness (parity for protected groups) (Yang et al., 2018). These assessments inform the trustworthiness of value-ranked feeds.
• Value Pluralism: Alexandria implements a pluralistic value interface, letting users articulate nuanced, multi-dimensional preferences, demonstrating empirically that richer value libraries allow users to express complex and sometimes contradictory priorities in feed curation (Kolluri et al., 16 May 2025).

4. Empirical Results and User Studies

Experiments consistently show that value-ranked feeds are recognizable to users and better align with their self-reported priorities than engagement-based rankings:

• In controlled studies, participants could identify value-ranked feeds with high accuracy (76.1% for single-value re-ranking, 63.4% for multi-slider configurations) (Jahanbakhsh et al., 17 Sep 2025).
• Users reported increased perceived control, satisfaction, and nuanced feed adjustments when provided with larger value libraries and rich interfaces (Kolluri et al., 16 May 2025).
• Value alignment produced objective shifts in feed composition away from the engagement-dominated baseline, surfacing a broader range of societal or personal values (e.g., “Caring” vs. “Achievement”) (Jahanbakhsh et al., 17 Sep 2025).
• Studies revealed cognitive and behavioral trade-offs: granular control increases curation effort; users desired greater transparency and guidance in balancing value sliders or interpreting model-driven feed outcomes (Malki et al., 13 Sep 2025, Kolluri et al., 16 May 2025).

5. Implementation Challenges and Limitations

While value-ranking enables more democratic and accountable feed curation, implementation exposes several challenges:

• Cognitive Load: User interfaces offering control over dozens of values can induce high cognitive demand; recognizability of value-ranked feeds drops as users adjust more sliders, and qualitative accounts signal difficulty in optimizing trade-offs among conflicting values (Jahanbakhsh et al., 17 Sep 2025).
• Value Labeling Reliability: The accuracy of LLM-based classifiers to score content varies; for some value dimensions, annotator disagreements or model errors persist (Jahanbakhsh et al., 17 Sep 2025, Kolluri et al., 16 May 2025).
• Inventory and Expressivity: Value-based re-ranking is limited by the value “coverage” of the platform's content. If a feed's inventory lacks posts expressing a target value, user adjustments have limited effect (Jahanbakhsh et al., 17 Sep 2025, Kolluri et al., 16 May 2025).
• Value Bubbles: There is a plausible implication that unbounded user control could lead to "value bubbles" analogous to echo chambers, especially if users exclusively prioritize specific values or suppress diversity (Jahanbakhsh et al., 17 Sep 2025, Kolluri et al., 16 May 2025).
• Scalability: Real-time LLM inference and large-scale content annotation can introduce computational overhead, although server-side implementations with caching and parallelization can mitigate latency (Kolluri et al., 16 May 2025).
• Governance and Manipulation: Community-driven curation or open “marketplaces of values” carry challenges related to curation, value definition, and potential centralization or manipulation risks (Kolluri et al., 16 May 2025).

6. Future Directions and Societal Implications

The advancement of value-ranked feeds has significant ramifications for platform governance, user autonomy, and the design of information systems:

• Broader Societal Impact: By embedding explicit values—ranging from individual preferences to negotiated social norms—value-ranked feeds can counteract the algorithmic dominance of engagement and address broader goals such as well-being, civic discourse, and equity (Kolluri et al., 16 May 2025).
• Democratic and Transparent Curation: Mechanisms for user control, transparency, and auditability are repeatedly emphasized as necessary for ethical implementation and for mitigating the risk of unintended consequences or overcentralization (Malki et al., 13 Sep 2025, Kolluri et al., 16 May 2025, Yang et al., 2018).
• Integration with Platform Algorithms: Value-aligned ranking may become a complement or even a replacement for black-box engagement optimization, with possible hybrid solutions balancing relevance, value alignment, diversity, and utility.
• Further Research: Areas for development include improved interfaces for balancing multiple values, cross-cultural generalization of value frameworks, mitigation strategies for value bubbles, and scalable, privacy-preserving value labeling.

Value-ranked feed design thus represents a maturing research and application area that reframes feed curation as a multi-objective, participatory, and societally-aware optimization problem, shifting the paradigm from opaque engagement steering toward transparent, value-aligned, and democratic content delivery across digital platforms.