How do you balance exploration and exploitation in recommender systems with cold start users or items?

1 What is exploration and exploitation?

Exploration and exploitation are two fundamental trade-offs in recommender systems. Exploration means trying out new or unknown items or users to learn more about their preferences or characteristics. Exploitation means using the existing knowledge or data to recommend the best items or users based on their predicted ratings or outcomes. Ideally, a recommender system should balance exploration and exploitation to achieve both short-term and long-term goals. However, finding the optimal balance is difficult, especially when there is uncertainty or noise in the data.

2 Why is cold start a problem?

Cold start is a problem because it reduces the effectiveness and efficiency of recommender systems. For new users, the system does not have enough information about their preferences, tastes, or needs, so it cannot recommend relevant items or services to them. For new items, the system does not have enough feedback or ratings from existing users, so it cannot estimate their quality or popularity. This leads to poor user satisfaction, low conversion rates, and missed opportunities. Moreover, cold start can also affect the performance of existing users or items, as the system may neglect them in favor of exploring new ones, or vice versa.

3 How to deal with cold start users?

One way to deal with cold start users is to use demographic or contextual information to infer their preferences or needs. For example, the system can use the user's age, gender, location, device, or time of day to recommend items or services that are likely to match their profile or situation. Another way is to elicit explicit feedback from the user, such as asking them to rate some items, answer some questions, or provide some preferences. This can help the system to learn more about the user and personalize the recommendations accordingly. However, both methods have some drawbacks, such as privacy concerns, user fatigue, or bias.

4 How to deal with cold start items?

One way to deal with cold start items is to use content-based or feature-based information to describe their attributes or characteristics. For example, the system can use the item's title, description, category, price, or image to recommend items that are similar or complementary to the user's previous choices or interests. Another way is to leverage social or collaborative information to promote the items to potential users. For example, the system can use the item's popularity, reviews, ratings, or endorsements from other users or experts to recommend items that have high quality or credibility. However, both methods have some limitations, such as data sparsity, scalability, or diversity.

5 How to balance exploration and exploitation?

One way to balance exploration and exploitation is to use multi-armed bandit algorithms. These are algorithms that dynamically allocate resources among different options or actions based on their estimated rewards or outcomes. For example, the system can use a bandit algorithm to decide whether to recommend a new or an existing item to a user, based on their expected ratings or conversions. The bandit algorithm can balance exploration and exploitation by using various strategies, such as epsilon-greedy, softmax, UCB, or Thompson sampling. However, bandit algorithms have some challenges, such as delayed feedback, contextual dependency, or non-stationarity.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?