Recommender systems are everywhere. Netflix recommends movies, Spotify recommends music, Amazon recommends products, YouTube recommends videos, and social media platforms recommend posts, accounts, and communities.
Most of these systems become better as they collect more information about users and items. They learn what users click, purchase, watch, skip, rate, save, or ignore.
But what happens when there is no history?
What should an application recommend to a user who registered ten seconds ago?
How should an e-commerce platform decide who might be interested in a product that was added five minutes ago?
And what happens when we launch an entirely new platform where there are no users, interactions, ratings, or historical patterns at all?
These situations create what is known as the cold start problem.
Cold start is one of the fundamental challenges in recommender-system engineering because many recommendation algorithms depend heavily on historical interaction data. Understanding cold start is therefore important not only for data scientists, but also for software engineers designing applications that contain personalization, search, ranking, recommendation, or discovery features.
In this article, we will explore what cold start is, where the concept came from, the different types of cold start, how recommender systems handle it, common solutions, and how we can incorporate cold-start strategies into our existing software development process.
What Is Cold Start in Recommender Systems?
Cold start describes a situation where a recommender system does not have enough information to generate reliable personalized recommendations.
Consider a movie recommendation system.
After a user has watched hundreds of movies, the system may know that the user:
- frequently watches science-fiction movies,
- likes Christopher Nolan films,
- rarely watches romantic comedies,
- usually watches documentaries on weekends,
- finishes most space-related movies,
- and frequently gives thrillers a positive rating.
The recommendation engine has a rich behavioral profile that it can use.
Now imagine another user who has just created an account.
The system knows almost nothing about that person.
There are no:
- movie views,
- ratings,
- searches,
- likes,
- skips,
- watch-time records,
- or browsing patterns.
The recommendation system is effectively starting from zero.
This is a user cold start.
Cold start can also occur on the other side of the recommendation relationship.
Imagine that a new movie has just been added to the platform.
No users have watched it yet.
If the recommendation algorithm depends primarily on collaborative behavior such as:
People who watched Movie A also watched Movie B
the new movie has no interaction history and therefore cannot easily participate in those calculations.
This is an item cold start.
At its core, cold start is therefore a lack-of-information problem.
Why Does Cold Start Happen?
Many recommender systems learn from relationships between users and items.
Imagine a simplified interaction matrix:
| User | Movie A | Movie B | Movie C | Movie D |
|---|---|---|---|---|
| User 1 | 5 | 4 | ? | 2 |
| User 2 | 5 | 5 | 4 | ? |
| User 3 | ? | 4 | 5 | 1 |
| New User | ? | ? | ? | ? |
The system can detect similarities between Users 1, 2, and 3 because they have provided information through ratings or behavior.
The new user has contributed nothing.
There is no statistical relationship to analyze.
A similar problem occurs with a newly added item:
| User | Movie A | Movie B | Movie C | New Movie |
|---|---|---|---|---|
| User 1 | 5 | 4 | 3 | ? |
| User 2 | 4 | 5 | 4 | ? |
| User 3 | 3 | 4 | 5 | ? |
Pure collaborative filtering has very little information with which to determine who should receive the new movie as a recommendation.
This is why cold start is especially problematic for systems that rely heavily on collaborative filtering.
A Brief History of the Cold Start Problem
The cold start problem grew naturally out of the development of collaborative filtering.
Early recommender systems began becoming prominent during the 1990s as researchers looked for ways to help users navigate increasing amounts of online information.
One influential example was GroupLens, introduced in the 1990s for filtering Usenet news articles. GroupLens used collaborative filtering principles: users who had agreed on articles in the past could help predict which other articles they might like in the future.
This approach introduced an obvious dependency:
Collaborative filtering needs collaboration data.
Without ratings, clicks, purchases, or other interactions, there is little information from which relationships can be calculated.
As recommendation research matured, researchers increasingly studied the problem explicitly. A notable milestone was the 2002 paper “Methods and Metrics for Cold-Start Recommendations” by Andrew Schein, Alexandrin Popescul, Lyle Ungar, and David Pennock. The researchers examined recommendations for items that had not yet received ratings and explored methods combining collaborative and content information.
Since then, cold start has remained an important research and engineering problem.
Modern approaches have expanded beyond traditional collaborative filtering and now include:
- content-based recommendation,
- hybrid recommendation systems,
- demographic information,
- contextual signals,
- embeddings,
- neural networks,
- transfer learning,
- multi-armed bandits,
- graph-based recommendation,
- and increasingly semantic representations generated from text, images, audio, and other content.
Despite increasingly sophisticated algorithms, the fundamental challenge remains unchanged:
How can we make a good prediction when we have very little evidence?
The Main Cold Start Cases
Cold start is often described as two main cases: new user and new item.
In production systems, however, it is useful to think about at least three major categories.
1. New User Cold Start
A new user enters the application without an interaction history.
For example, someone creates an account on a music streaming platform.
The platform does not yet know:
- preferred artists,
- favorite genres,
- listening times,
- skipped songs,
- completed songs,
- playlists,
- or search behavior.
Collaborative filtering cannot easily identify similar users because there is no behavioral profile to compare.
This is probably the most recognizable form of cold start.
One common solution is to collect a small amount of information during onboarding.
For example:
Choose three genres you like:
- Rock
- Jazz
- Classical
- Hip Hop
- Electronic
- Country
Or:
Choose five movies you enjoyed.
The recommendation engine can immediately use those selections to create an initial preference profile.
Netflix, for example, explains that new profiles may select titles they like to help “jump start” recommendations. If the user does not choose titles, Netflix can initially provide a diverse and popular selection. As the user interacts with the service, behavioral signals increasingly drive recommendations.
This illustrates an important recommender-system principle:
Cold-start recommendations do not have to be perfect. They need to be good enough to encourage the interactions that will improve future recommendations.
2. New Item Cold Start
The second case occurs when a new item enters the catalog.
Examples include:
- a newly released movie,
- a new product,
- a recently published article,
- a new restaurant,
- a new job posting,
- a newly uploaded video,
- or a new song.
The system may know a lot about existing users but almost nothing about how users will interact with the new item.
For collaborative filtering, this is particularly challenging because the item has no collaborative history.
However, unlike a completely unknown user, items often have useful metadata.
A movie may have:
- title,
- genre,
- director,
- actors,
- description,
- release year,
- language,
- keywords,
- and content rating.
An e-commerce product may have:
- category,
- brand,
- description,
- price,
- specifications,
- color,
- manufacturer,
- and images.
A content-based recommender can use these attributes even before anyone interacts with the item.
3. New System or New Community Cold Start
The most extreme cold-start case happens when the recommendation system itself is new.
Imagine launching a new marketplace.
There may initially be:
- 50 products,
- 20 customers,
- very few purchases,
- almost no ratings,
- and no meaningful behavioral history.
In this situation, both users and items are cold.
This is sometimes called the new-community problem or system cold start.
Pure collaborative filtering is usually ineffective because the interaction matrix is nearly empty.
Early recommendations may therefore depend heavily on:
- popularity,
- curated recommendations,
- business rules,
- content similarity,
- demographic information,
- contextual information,
- imported historical data,
- or external datasets.
As the platform collects interactions, machine-learning models can gradually replace or complement these initial strategies.
Strict Cold Start vs. Partial Cold Start
Cold start does not always mean that absolutely no information exists.
It can exist on a spectrum.
Strict Cold Start
There is essentially no historical information.
For example:
New user:
Clicks = 0Purchases = 0Ratings = 0Searches = 0
Partial Cold Start
A small amount of data exists, but not enough to build a reliable profile.
For example:
Clicks = 3Purchases = 0Ratings = 1
Production systems should usually distinguish between these states.
A user with zero interactions may receive popularity-based recommendations.
After three interactions, the system might start blending content-based personalization.
After twenty interactions, collaborative filtering may become reliable.
This creates a progressive recommendation strategy rather than treating every user identically.
How Does a Recommender System Handle Cold Start?
There is no single universal cold-start algorithm.
Instead, systems typically combine several strategies.
Popularity-Based Recommendations
The simplest solution is recommending what is currently popular.
For example:
New User ↓No history available ↓Retrieve popular items ↓Apply basic filters ↓Return recommendations
Possible signals include:
- most purchased,
- most watched,
- trending,
- highest rated,
- most clicked,
- recently popular,
- or popular within a geographic region.
Popularity provides a strong baseline because popular items are statistically more likely to appeal to a random user than randomly selected items.
However, it provides limited personalization.
Content-Based Filtering
Content-based systems examine the characteristics of items.
Suppose a user selects:
Interstellar
The system might examine features such as:
Genre: Science FictionDirector: Christopher NolanTopics: Space, Time, ExplorationRelease period: 2010s
It can then find similar items.
Content-based recommendation is particularly useful for item cold start because new items may have rich metadata even if they have no interaction history.
Modern systems can also represent content as embeddings.
For example:
Item description ↓Embedding model ↓Vector representation ↓Vector similarity search ↓Similar items
This can allow a new item to immediately participate in recommendations.
User Onboarding
Instead of waiting for users to generate behavioral signals, we can ask them directly.
Examples include:
- Select topics you are interested in.
- Choose five movies you like.
- Follow at least three creators.
- Select your favorite categories.
- Tell us your preferred price range.
The result may become an initial preference vector.
New User ↓Onboarding Preferences ↓Initial User Profile ↓Content Matching ↓Initial Recommendations
The important UX consideration is not to make onboarding so long that users abandon the application.
There is usually a tradeoff:
More onboarding questions ↓Better initial personalization ↓Higher onboarding friction
The best systems collect the minimum amount of information necessary to produce useful initial recommendations.
Demographic and Contextual Information
Some applications can use contextual information such as:
- country,
- language,
- device,
- time of day,
- location,
- age range when appropriate and permitted,
- selected interests,
- referral source,
- current page,
- or session behavior.
For example:
New UserLanguage: EnglishCountry: United StatesDevice: MobileCurrent category: Running Shoes
Even without long-term history, the system can make more useful recommendations than simply returning random products.
Privacy and fairness must be carefully considered whenever demographic information is involved.
Hybrid Recommendation Systems
Hybrid systems combine multiple recommendation strategies.
For example:
Recommendation Score = Collaborative Score + Content Score + Popularity Score + Context Score
The weights can change depending on how much information exists.
For a new user:
Popularity 50%Content 35%Context 15%Collaborative 0%
After the user generates more activity:
Popularity 10%Content 25%Context 10%Collaborative 55%
This dynamic weighting is extremely useful in production systems.
Research literature frequently identifies hybrid methods as an important way to address cold-start limitations because they can use content or auxiliary information while collaborative data remains sparse.
Exploration and Exploitation
Another important concept is deciding whether to recommend something the system already believes the user will like or experiment with something uncertain.
This is the classic exploration vs. exploitation problem.
Exploitation
Recommend items that already have a high predicted probability of success.
Example:
The user likes Java programming, so recommend another Java article.
Exploration
Occasionally recommend something less certain.
Example:
The user likes Java, but let’s test whether they are also interested in Kubernetes.
If the user clicks the Kubernetes article, the system has discovered an additional preference.
Techniques such as multi-armed bandits can help balance exploration and exploitation.
This is especially valuable for cold start because the recommendation system needs to actively collect information.
Cold Start as a Feedback Loop
One of the best ways to understand the problem is as a feedback loop.
Initial Recommendation ↓User Interaction ↓Collect Signals ↓Update User Profile ↓Improve Recommendation ↓More Interaction ↓More Data ↓Better Recommendation
Cold start is primarily the challenge at the beginning of this loop.
Once sufficient interactions are collected, the system becomes increasingly personalized.
Therefore, a good cold-start strategy does more than generate useful recommendations.
It should also accelerate learning.
What Signals Should We Collect?
Different interactions have different strengths.
Explicit Signals
The user deliberately tells the system something.
Examples:
- ratings,
- likes,
- dislikes,
- selected interests,
- favorites,
- reviews.
For example:
User rated Item A = 5 stars
This is a strong signal.
Implicit Signals
The application observes user behavior.
Examples:
- clicks,
- purchases,
- watch time,
- scroll depth,
- search queries,
- add-to-cart events,
- article completion,
- video completion,
- repeated views,
- skips.
For example:
Video startedVideo watched for 45 minutesVideo duration = 47 minutes
This may strongly indicate interest even though the user never clicked a Like button.
Modern recommendation systems commonly rely heavily on implicit feedback because it can be collected continuously without requiring additional effort from users.
Example: E-Commerce Cold Start
Imagine an online store.
A new visitor arrives.
There is no profile.
The initial recommendation pipeline might be:
Request ↓Is user known? ↓No ↓Determine current context ↓Retrieve popular products ↓Apply category relevance ↓Apply availability filter ↓Apply geographic constraints ↓Rank products ↓Return recommendations
After the user searches for:
mechanical keyboard
the recommendation strategy changes.
The application now knows something about the current intent.
Recommendations might include:
- mechanical keyboards,
- switches,
- keycaps,
- wrist rests,
- keyboard cables.
After additional browsing and purchases, the recommendation engine can transition toward collaborative personalization.
Example: Software Engineering Blog
Cold start is not limited to massive platforms like Netflix or Amazon.
Imagine a technical blog containing articles about:
- Java,
- Spring Boot,
- Kubernetes,
- AI,
- databases,
- software architecture,
- testing,
- security,
- and DevOps.
A completely new visitor arrives.
There is no profile.
The site might initially display:
Most Popular ArticlesTrending This WeekRecently PublishedEditor's Picks
The visitor reads:
Feature Flags in Software Development
The recommender can now infer interest in:
Software DeliveryDevOpsRelease StrategiesContinuous Delivery
The next recommendations could include:
Dark LaunchesA/B TestingCanary ReleasesBlue-Green DeploymentsFeature Toggles
As the visitor reads more articles, the recommendations become increasingly personalized.
This illustrates an important point:
You do not need enormous amounts of AI infrastructure to implement useful cold-start logic.
Simple business rules combined with good event tracking can provide substantial value.
How Can We Integrate Cold-Start Handling into Our Software Development Process?
Cold start should not be treated as a problem that belongs exclusively to a machine-learning team.
It affects:
- architecture,
- UX,
- APIs,
- databases,
- analytics,
- observability,
- product design,
- testing,
- and deployment.
A practical implementation can be divided into several stages.
Step 1: Define Recommendation Scenarios
First determine where recommendations exist.
Examples:
Homepage recommendationsRelated productsRelated articlesPeople to followRecommended videosRecommended jobsRecommended courses
For each scenario, ask:
What happens if we know nothing about the user?
and:
What happens if the item has no interactions?
These questions should become part of the feature’s requirements.
Step 2: Define Cold-Start States
Instead of simply checking whether a user is new, define meaningful states.
For example:
COLD0 interactionsEARLY1–5 interactionsLEARNING6–20 interactionsWARM20+ interactions
Your thresholds will depend on the application.
The recommendation service can expose this state internally:
{ "userId": "12345", "recommendationState": "EARLY", "interactionCount": 4}
Different strategies can then be applied.
Step 3: Build a Reliable Fallback
Every recommendation endpoint should have a fallback.
For example:
Personalized Model ↓Enough data? ↙ ↘ Yes No ↓ ↓Model PopularityResults Results
Never allow the application to display:
No recommendations available.
simply because the machine-learning model lacks data.
Fallbacks can include:
- popular items,
- trending items,
- recent items,
- editor selections,
- category-based items,
- or business-curated recommendations.
Step 4: Build an Event Collection Layer
Recommendation quality depends heavily on telemetry.
Create consistent events such as:
ITEM_VIEWEDITEM_CLICKEDITEM_LIKEDITEM_DISLIKEDITEM_PURCHASEDITEM_SKIPPEDSEARCH_PERFORMEDITEM_SAVEDITEM_COMPLETED
An event might look like:
{ "event": "ITEM_VIEWED", "userId": "12345", "itemId": "98765", "timestamp": "2026-08-26T18:30:00Z", "context": { "source": "recommendation", "position": 3 }}
These events become the raw material for future personalization.
Step 5: Create Item Metadata
Good item metadata dramatically improves item cold start.
Depending on the application, metadata might include:
categorytagsdescriptionkeywordsauthorbrandlanguagepricetopicslocationcreation date
For text-heavy applications, embeddings can supplement manually assigned metadata.
For example:
Article published ↓Generate embedding ↓Store vector ↓Find semantically similar articles ↓Article immediately becomes recommendable
The item does not need to wait for thousands of clicks before appearing in useful recommendations.
Step 6: Add a Recommendation Service
Avoid embedding recommendation logic directly throughout the frontend.
Create a clear service boundary.
For example:
Frontend ↓Recommendation API ↓Recommendation Strategy ├── Cold User Strategy ├── Early User Strategy ├── Personalized Strategy ├── Cold Item Strategy └── Fallback Strategy
An API might look like:
GET /api/recommendations?userId=123&type=homepage
Internally:
RecommendationService ↓Determine user state ↓Select strategy ↓Generate candidates ↓Rank candidates ↓Apply business rules ↓Return results
This architecture makes algorithms easier to evolve without rewriting the user interface.
Step 7: Use the Strategy Pattern
Cold-start logic is a good candidate for the Strategy Pattern.
Conceptually:
RecommendationStrategy├── PopularityRecommendationStrategy├── ContentRecommendationStrategy├── CollaborativeRecommendationStrategy└── HybridRecommendationStrategy
The system selects a strategy depending on available data.
For example:
if (interactionCount == 0) { return popularityStrategy.recommend(user);}if (interactionCount < 10) { return contentStrategy.recommend(user);}return hybridStrategy.recommend(user);
Real implementations will usually be more sophisticated, but this architecture separates concerns effectively.
Step 8: Separate Candidate Generation from Ranking
Production recommender systems often use two broad stages.
Candidate Generation
Retrieve potentially relevant items.
Examples:
Popular itemsSimilar itemsCollaborative candidatesRecently trending itemsSemantic matches
Perhaps this generates:
10,000 candidates
Ranking
A ranking model determines which candidates are most useful.
10,000 candidates ↓Ranking ↓Top 20 recommendations
Cold-start strategies can participate in candidate generation alongside mature recommendation models.
This provides flexibility.
Step 9: Measure Cold Users Separately
One common mistake is evaluating the entire recommendation system with a single metric.
Suppose the average click-through rate is:
CTR = 8.3%
That number hides important information.
Instead measure:
Cold users: 3.1%Early users: 5.8%Warm users: 11.2%
Similarly, measure new items separately.
Useful metrics include:
- click-through rate,
- conversion rate,
- watch time,
- engagement rate,
- precision,
- recall,
- NDCG,
- coverage,
- diversity,
- novelty,
- time to first interaction,
- time to warm state.
One particularly useful product metric is:
How quickly does a user leave the cold-start state?
For example:
Median time to 5 meaningful interactions = 18 minutes
Reducing this time may improve the entire recommendation system.
Step 10: Use A/B Testing
Cold-start strategies should be experimentally evaluated.
For example:
Variant A
Popular Items
Variant B
Onboarding Interests + Popular Items
Variant C
Onboarding + Content Recommendations
Then compare:
CTRSession durationConversionRetentionNumber of interactionsCold-to-warm transition time
The technically most advanced algorithm is not necessarily the best product experience.
Step 11: Monitor Recommendation Quality
Recommendation systems can degrade silently.
Monitor signals such as:
Recommendation API latencyEmpty recommendation rateFallback usage rateCold-user percentageCold-item percentageCTRConversionModel confidenceCandidate count
A particularly useful metric is:
fallback_rate
If it suddenly increases from:
5%
to:
60%
the personalized recommender may have failed even though the API continues returning HTTP 200 responses.
Step 12: Continuously Improve the Model
A recommendation architecture should allow gradual evolution.
Phase 1
Popularity
Phase 2
Popularity + Categories
Phase 3
Content-Based Recommendation
Phase 4
Collaborative Filtering
Phase 5
Hybrid Recommendation
Phase 6
Embeddings + Learned Ranking
Phase 7
Exploration + Continuous Learning
This approach is usually more practical than attempting to build a sophisticated AI recommender before enough data exists.
A Practical Architecture
A simplified production architecture might look like this:
User
↓
Application
↓
Recommendation API
↓
Recommendation Router
↙ ↓ ↘
Cold User Warm User Cold Item
↓ ↓ ↓
Popularity Collaborative Content
+ Context Filtering Similarity
↘ ↓ ↙
Candidate Generator
↓
Ranker
↓
Business Rules
↓
Recommendations
↓
User Actions
↓
Event Stream
↓
Analytics / Storage
↓
Model Training
The key principle is that cold-start handling should be part of the architecture rather than an emergency fallback added later.
Common Mistakes
Several mistakes frequently appear when teams first implement recommender systems.
Random Recommendations
Random recommendations may provide diversity, but pure randomness generally creates poor user experiences.
Use popularity, context, or content similarity as a baseline.
Waiting for Enough Data
A team may say:
We will implement recommendations after we have enough data.
But recommendations themselves can generate the interactions needed for better recommendations.
Start with simple strategies and evolve them.
Too Much Onboarding
Asking users to rate 50 items may improve the initial profile but dramatically increase abandonment.
Ask for a small number of high-information preferences.
Ignoring New Items
A recommender that heavily favors items with historical interactions can create a self-reinforcing system:
Popular Item ↓More Recommendations ↓More Clicks ↓More Training Data ↓Even More Recommendations
Meanwhile:
New Item ↓No Interactions ↓No Recommendations ↓Still No Interactions
The new item never gets an opportunity to become popular.
Exploration strategies and content-based recommendation can help break this cycle.
Using Only Accuracy
A recommendation engine that always recommends extremely popular products may achieve respectable click-through rates while offering little discovery.
Evaluate:
- diversity,
- novelty,
- catalog coverage,
- fairness,
- and exposure,
in addition to accuracy.
Treating Cold Start as Only an ML Problem
Cold start affects the entire product.
Solutions can include:
UX+Data Collection+Backend Architecture+Business Rules+Machine Learning+Experimentation
Sometimes a simple onboarding screen can solve more of the problem than another six months of model development.
Best Practices
When designing cold-start handling, consider the following principles.
Always provide a fallback.
The recommendation system should produce something useful even if personalization fails.
Collect useful signals early.
Clicks, searches, views, skips, likes, and purchases quickly improve user understanding.
Keep onboarding short.
Collect enough information to get started without creating excessive friction.
Invest in item metadata.
Good metadata makes new items immediately usable by content-based systems.
Use hybrid approaches.
Do not force one algorithm to solve every recommendation scenario.
Separate cold and warm users.
Different information states require different strategies.
Allow exploration.
New users and new items require opportunities for the system to learn.
Measure cold-start performance separately.
Average recommendation metrics can hide serious cold-start problems.
Design cold start from the beginning.
Do not assume the recommendation model will somehow solve it automatically.
Cold Start and Modern AI
Modern AI does not eliminate the cold-start problem, but it gives us more tools to reduce it.
For example, large language models and embedding models can extract meaningful representations from:
- text,
- product descriptions,
- documentation,
- reviews,
- images,
- user queries,
- and other unstructured content.
Consider a newly published article titled:
Understanding Event-Driven Architecture with Apache Kafka
Even if nobody has read it yet, an embedding model may recognize semantic relationships with existing content about:
KafkaMessage QueuesMicroservicesEvent-Driven ArchitectureDistributed SystemsAsynchronous Communication
The article can immediately become a recommendation candidate.
Similarly, modern neural approaches can combine user attributes, item attributes, contextual signals, and behavioral histories in a single learned representation. Neural networks and hybrid approaches continue to be actively researched as methods for dealing with sparse and cold-start recommendation scenarios.
However, AI does not change the fundamental principle.
If the system knows nothing about the user, it must obtain useful information from somewhere.
That information might come from:
User onboardingContextItem contentPopulation behaviorExternal knowledgeSession behaviorExploration
Cold start is fundamentally an information problem.
Final Thoughts
The cold start problem is one of the most important practical challenges in recommender systems.
Recommendation algorithms become powerful when they have rich interaction histories, but every user, item, and platform starts somewhere.
The three major cold-start cases are:
- New User Cold Start — the system does not yet understand the user’s preferences.
- New Item Cold Start — the system does not yet know how users will react to a newly added item.
- New System or Community Cold Start — there is not enough historical information about either users or items.
There is no single algorithm that completely solves these problems.
Successful systems typically combine techniques such as:
Popularity+Content-Based Recommendation+Onboarding+Context+Collaborative Filtering+Hybrid Models+Embeddings+Exploration
From a software engineering perspective, perhaps the most important lesson is that cold start should be considered during system design rather than after a recommendation model has already been built.
We should define:
What happens with zero data?What happens with limited data?What happens with sufficient data?
Then create different strategies for each state.
A mature recommender system does not simply contain an intelligent model.
It contains an intelligent transition from knowing nothing to knowing enough.
And that transition is exactly what solving the cold start problem is about.
Recent Comments