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To reduce popularity bias and repeated recommendations, first identify what is being harmed, then address it at the stage where the problem arises: data, model training, preference elicitation, ranking, or the sequence of recommendations over time. Do not simply suppress popular items. Popularity can reflect genuine quality or user preference; it becomes a problem when it crowds out useful discovery, repeats familiar options, or unfairly limits exposure for other items or stakeholders.
When is popularity bias actually a problem?
A recommendation system is not biased merely because it suggests a popular item. High interaction counts can be evidence of broad appeal or quality, but they can also reflect promotion, position, price, or the fact that an item has already been shown more often. Popularity bias matters when recommendations focus on popular items so heavily that they limit the system’s value or harm a stakeholder, such as by crowding out relevant niche items or restricting provider exposure. That impact-based framing is central to a 2024 survey of 123 papers on popularity bias in recommender systems (Springer Nature survey).
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Repetition is related but distinct. A list can contain widely popular items without repeating the same item, while repeated exposure can occur even when the items are not among the most popular overall. Decide whether the issue is narrow exposure across the catalog, repetitive or near-identical suggestions, poor discovery, or a combination; the remedy and the measure of success depend on that diagnosis.
How do you find the source of the problem?
Trace the system’s path from recorded interactions to what a person sees. Popularity in logs may combine actual preference with visibility effects: if a system shows an item often, it can collect more interactions for that item, then use those interactions to justify showing it again. The survey describes popularity as both a possible preference signal and a feedback-loop risk (survey discussion).
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- Define the application and affected stakeholders. State what recommendations should accomplish and who might lose value from the current behavior: users, less-exposed item providers, or both.
- Measure popularity and exposure separately. Compare item interaction counts and popularity distribution in training data with how often items are surfaced in recommendations. Where the application supports it, examine exposure across popularity groups rather than relying only on a catalog-wide average.
- Check data representation and logging. Look for missing interaction coverage, skewed representation, and whether position or prior exposure could explain high counts. Do not infer quality from interaction volume alone.
- Inspect candidate generation and ranking. Determine whether less-popular items are absent before ranking or appear as candidates but are pushed down by scoring. This identifies whether to focus on data or model changes versus reranking.
- Measure repeats across time. Track identical items and similarity between successive lists or sessions, not just diversity within one list. A single-list metric will not reveal every repeated-exposure pattern.
- Write down observable evidence of harm. Examples include relevant niche items losing exposure, the same options resurfacing, or users finding discovery less useful. Choose measures that test the harm you identified.
Where should you intervene?
Mitigation can happen before training, during learning, while eliciting preferences, after candidate scoring, or across a session. The survey distinguishes pre-processing, in-processing, and post-processing approaches and notes that in-process methods jointly consider competing objectives most often in the literature (Springer Nature survey).
| Intervention point | What to do | Best fit and trade-off |
|---|---|---|
| Before training | Audit data coverage and skew; consider addressing representation problems or reweighting data. | Useful when logs or item representation are distorted. Avoid deleting or downweighting popular items automatically: their popularity may reflect real user preference or quality. |
| During model learning | Incorporate popularity-aware regularization, constraints, or objectives alongside relevance. | Useful when the learned model itself over-favors popularity. Tune the intervention against the application’s relevance needs and the specific harm being addressed; no universal setting is established. |
| Preference elicitation | Explore a broader range of items or preference signals instead of asking only about likely hits. | Useful when the feedback gathered to infer interests is already popularity-skewed. Exploration can broaden the inferred preference picture, though it should be evaluated for the application. |
| After candidate scoring | Rerank candidates using list-level diversity, novelty, or exposure objectives, while retaining a relevance floor. | Useful when relevant alternatives reach the candidate set but ranking repeatedly favors the same kinds of items. More diversity can come at an accuracy cost. |
| Across a session | Monitor repeated items and similarity between consecutive lists; adjust diversity in response to interaction history. | Useful when the problem emerges over time rather than within one list. Dynamic strategies need validation beyond simulated interactions before their engagement effects can be assumed in deployment. |
How can preference elicitation avoid learning only familiar tastes?
Bias can enter before the recommendation list exists. If a system asks for feedback only on options it already expects a person to like, its picture of that person’s preferences may remain narrow. A 2021 Google Research paper studies multi-armed-bandit diversification as a way to broaden preference elicitation and reports that popularity bias in elicitation contributes to popularity bias in recommendations (Google Research publication).
The practical implication is to treat feedback collection as part of recommendation design. Explore enough to learn about interests outside the system’s existing high-confidence assumptions, then assess whether that exploration yields useful recommendations rather than simply more varied ones. The cited work establishes a studied approach, not a universal recipe for every product or user population.
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At ranking time, diversification can discourage a list from clustering around near-identical features or repeatedly surfacing familiar options. Feature-based reranking is one way to balance list variety with candidate relevance. Novelty, diversity, and serendipity are not interchangeable: a list can be varied without containing useful surprises, so select the objective that corresponds to the problem you observed.
The serendipity-oriented greedy (SOG) algorithm is one published example. Its authors report stronger diversity and serendipity than comparison algorithms, while also identifying a trade-off against accuracy-oriented algorithms (Kotkov, Veijalainen, and Wang’s paper, first published in 2018 and included in the 2020 volume of Computing). This is evidence about a particular approach, not a guarantee that any diversity reranker will improve discovery without reducing relevance.
For repeat exposure, add history to the ranking decision: measure whether the same items recur and whether successive lists are too similar for the application. A session-level policy can respond to accumulated exposure rather than treating every list as independent. Avoid imposing a universal repeat cap or diversity quota; the reviewed literature does not establish one correct threshold for all applications.
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Do not judge a mitigation by one headline metric. Compare alternatives on the same data and interaction horizon, and report relevance alongside the outcomes the intervention is meant to improve.
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- Intra-list diversity: How varied are the items within an individual list?
- Novelty and serendipity: Are recommendations less familiar or unexpectedly useful, rather than merely different?
- Coverage and exposure: Do more catalog items or popularity groups receive exposure, and are particular stakeholders disadvantaged?
- Repetition over time: How often do identical items recur, and how similar are successive lists or sessions?
Segment results where relevant by user group, item popularity, and stakeholder; an overall gain can conceal a loss for a subgroup or a part of the catalog. The survey finds varied metrics and thresholds across studies, so metrics and success criteria need to fit the application rather than being treated as universal standards (survey on evaluation and context dependence).
Use offline evaluation to screen ideas and make comparisons reproducible, then gather human evidence through user evaluation, experiments, or field studies to test whether people actually find the recommendations more useful and less repetitive. The survey reports that computational and offline experiments dominate this literature, while human-in-the-loop and real-world studies are comparatively rare. Offline metric improvements alone therefore do not establish user benefit.
What do the latest engagement results establish?
A 2026 ACM paper on dynamic, fine-grained diversity control (DDIR) reports a 4.35% increase in average session length and a 25.65% increase in long-term engagement over state-of-the-art baselines in the KuaiRand simulated environment (Li et al., published August 14, 2026). These are simulation findings, not live deployment results or a production guarantee. The study’s central premise is that moderate homogeneity may be useful early in an interaction and harmful later, which supports testing diversity policies that adapt over time instead of assuming a fixed level is always best.
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