Recommender systems are used to provide personalized experiences for customers in many industries today, including e-commerce, social media, entertainment, and education. These recommender systems make recommendations based on user preferences and collect user feedback to optimize their performances.
Netflix uses your watch history and the preferences of similar viewers to determine what you might like to watch next. That’s why Netflix nudges you to watch Prison Break after completing Money Heist.
According to Statista, by the end of 2024, Netflix had over 300 million paid subscribers, so its recommender system has significant user data to work with. Hence, the intelligent movie recommendations.
But a platform with a newly implemented recommender system that has insufficient information to interact with will face what is known as the cold start problem. This means that the platform will be unable to efficiently and accurately recommend products or services that meet the needs of its users.
In this article, you’ll learn about the cold start problem in recommender systems, its types, why it occurs, and how it can be mitigated.
What is the Cold Start Problem?
The cold start problem in recommender systems occurs when there is little or no historical data to draw inferences from. This means the recommender system cannot accurately provide relevant suggestions to users when initially implemented on a new platform since it takes time to gather data and draw insights from it.
Typically, recommender systems gather data such as product interactions, purchases, reviews, and so on, depending on the business’ key data points. This data is called the reference characteristics of the system. The system trains on this data to provide intelligent suggestions that will compel users to continue using the platform.
For example, Spotify’s recommender system can analyze your listening history and play frequency to understand your past music preferences and predict what you might like to listen to next.
The cold start problem is a popular problem that data scientists and machine learning (ML) engineers face when building recommender systems for a business. The performance of the recommender system drops when there is no sufficient data to gather from new users or items on the platform. Sadly, this low performance can turn users away and lead to revenue loss.
Types of Cold Start Problems in Recommender Systems
There are two main types of cold start problems in recommender systems: the user cold start and the item cold start. To illustrate these problems, I created a table representing a recommendation system based on user-item ratings. In this table:
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Rows represent users
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Columns represent items
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Matrix represents users’ ratings on a scale of 1 to 5.
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“–” represents unrated items.
User Cold Start
User cold start problem occurs when new users have not provided enough basic information or past interactions for the recommender system to make intelligent suggestions. Thus, the system cannot accurately predict the user’s possible interests.
As shown in the table above, the NEW USER has not used or evaluated any item. This means that the system cannot accurately predict what item the new user would most likely be interested in.
This is a serious problem because if new users keep getting off-target recommendations initially, they might stop using the platform before the recommender system has enough data from them to perform better.
Item Cold Start
The item cold start problem occurs when a new item or product, or more content, is added to a platform, but there are not enough ratings, purchases, or reviews for the item to be recommended.
As shown in the table above, item E is new and does not have any user ratings. Thus, the recommender system will not recommend this item to users.
Note that the item cold start problem does not only affect new items. It can also impact already existing but unpopular items. If an existing item has only had a few interactions, the recommender system does not have enough historical user feedback to understand the item metadata and user preferences. This means that the system will make poor recommendations, giving the item less visibility.
Challenges of the Cold Start Problem in Recommender Systems
Stereotypical recommendations
Relying on limited data can lead to stereotypical recommendations in recommender systems. For example, when the system only uses basic user actions, it can end up offering the same type of content or items repeatedly, based on generalized assumptions. This stereotype can push users away, especially if they start feeling like their interests are not being fully understood.
High churn rate
When a user has to scroll endlessly to find items they want because the recommender system does not surface relevant items, the platform is more prone to experiencing a high churn rate. This means that the platform may lose many of its users if they cannot quickly find relevant products or services.
Loss of customer loyalty
Statista reported that in 2023, 56% of consumers preferred to return to a retailer who provided a personalized shopping experience. This means that a lack of a customized experience through intelligent recommendations will make users mistrust the system’s ability to understand their needs. This mistrust can lead to user frustration, loss of customer loyalty, and, ultimately, negative brand perception.
How to Solve the Cold Start Problem in Recommender Systems
Below are some strategies that AI researchers have proposed to help mitigate user cold start and item cold start problems, respectively:
How to Solve User Cold Start
Collecting user preferences during sign-up
One way to fix the lack of historical data from users is to provide a questionnaire upfront when new users register for a platform. This questionnaire can help businesses obtain some basic preferences so they can build a helpful user profile and make initial recommendations.
Spotify uses this method to avoid user cold start problems. When you sign up, Spotify will ask you to select your favorite artists and music genres from a list of options. Spotify’s recommender system then uses this information to understand the type of songs you might like and build an initial playlist for you.
Still, businesses need to implement this onboarding strategy carefully, because if they ask new users too many questions upon registration, they might skip the questions or abandon the platform.
Using contextual data
Contextual data focuses on information like user location, demographics, device type, or real-time behavior. Businesses can obtain this data through the user’s sign-up information, IP Address, cookies, and browser settings. These extra insights can help businesses enhance the experience for new users by customizing their content and recommendations.
Travel platforms like Booking.com use this strategy to provide personalized recommendations and display localized content to new users. When first-time visitors access the site, Booking.com obtains contextual data through their IP Address, browser settings, and cookies. Using this information, Booking.com can recommend nearby accommodations, attractions, and travel deals in the user’s area. In the screenshot above, I have not signed in or registered on Booking.com, but the site already recommends content for my location.
Using social network-based data
Businesses can solve the user cold start problem by having new users register with their social logins. With this access, the recommender system can retrieve the user’s interests, past interactions, and behavior from their social profiles. This information helps the system to understand the user’s preferences and make suggestions accordingly.
How to Solve Item Cold Start
Leveraging content-based filtering
Content-based filtering is a recommendation technique that uses the characteristics or metadata of an item, such as features, genres, categories, or descriptions, to make recommendations.
By analyzing the item’s information, the recommender system can still suggest new or unpopular items to users even though the items have little reviews or interactions.
Note that content-based filtering can suffer poor recommendation quality when there are insufficient item characteristics. So, a business should only leverage this method if there is detailed information on the items.
Using hybrid filtering
Hybrid filtering involves combining the advantages of content-based filtering and collaborative filtering. Collaborative filtering is a recommendation technique that predicts a user’s preferences based on the behavior of similar users. It analyzes data such as browsing history, purchase history, and item ratings to identify users with similar interests. Then, the system suggests items those users have liked to new users.
You have likely seen this technique in action through features like “People Also Liked” or “People Also Searched.” Beyond the user-based recommendations, collaborative filtering also suggests items similar to those a user has previously engaged with.
The hybrid filtering approach alternates between the content-based filtering we have discussed above and collaborative filtering when one lacks more data than the other. For example, Amazon might recommend items based on product descriptions and categories (content-based filtering). Then, after some purchase history, the recommender system might suggest products based on what users with similar shopping habits bought (collaborative filtering).
Showing new releases on the homepage
Promoting new items or content on the homepage can provide visibility and encourage users to interact with the item. For maximum impact, it is best to highlight the new item in a visible section on the homepage and clearly label the item as new. This way, customers will not miss the update and are more inclined to try it out.
Conclusion
In this article, you have learned that the cold start problem is one of the key challenges that recommender systems face. Tackling this issue requires a combined approach of data analysis and continuous improvement. By applying the strategies discussed above, businesses can improve their recommender systems and offer more relevant, personalized experiences.
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