How do you incorporate user preferences and context into cold start recommendations?

1 What is the cold start problem?

The cold start problem occurs when a recommender system has to generate recommendations for a new user or a new item that has no or very few interactions with other users or items. This makes it difficult to apply collaborative filtering methods that rely on finding similar users or items based on their ratings or feedback. The cold start problem can be classified into three types: user cold start, item cold start, and system cold start. User cold start happens when a new user joins the system and has not provided any ratings or feedback yet. Item cold start happens when a new item is added to the system and has not received any ratings or feedback yet. System cold start happens when a new system is launched and has no or very few ratings or feedback from any users or items.

2 How to use user preferences?

One way to address the user cold start problem is to use user preferences to generate recommendations. User preferences can be collected explicitly or implicitly. Explicit preferences are obtained by asking users to provide ratings, feedback, or personal information, such as demographics, interests, or goals. Implicit preferences are inferred from users' behavior, such as browsing, clicking, purchasing, or searching. User preferences can be used to create user profiles that represent their tastes, needs, or intentions. User profiles can then be used to match users with similar profiles or items that fit their profiles, using content-based filtering or hybrid filtering methods.

3 How to use context?

Another way to address the cold start problem is to use context to generate recommendations. Context refers to any information that can influence the user's preferences or decision making, such as location, time, weather, mood, social situation, or device. Context can be used to enhance the user profiles or the item descriptions, by adding contextual features or dimensions. Context can also be used to filter or rank the recommendations, by applying contextual rules or models. For example, a recommender system can use context to recommend nearby restaurants that are open at the current time, or movies that suit the user's mood or social situation.

4 How to use item features?

One way to address the item cold start problem is to use item features to generate recommendations. Item features are descriptive attributes of the items, such as genre, category, price, or quality. Item features can be extracted from the item metadata, such as titles, tags, or descriptions, or from external sources, such as reviews, ratings, or social media. Item features can be used to create item profiles that represent their characteristics, value, or popularity. Item profiles can then be used to match items with similar profiles or users that like their profiles, using content-based filtering or hybrid filtering methods.

5 How to use feedback mechanisms?

Another way to address the cold start problem is to use feedback mechanisms to generate recommendations. Feedback mechanisms are ways to collect ratings or feedback from users after they receive or consume the recommendations. Feedback mechanisms can be explicit or implicit. Explicit feedback mechanisms are those that ask users to provide ratings, feedback, or comments, such as stars, likes, or reviews. Implicit feedback mechanisms are those that infer ratings or feedback from users' behavior, such as views, clicks, purchases, or dwell time. Feedback mechanisms can be used to update the user profiles or the item profiles, by adding or modifying their features or dimensions. Feedback mechanisms can also be used to evaluate the recommendation quality and user satisfaction, by measuring metrics such as accuracy, diversity, novelty, or relevance.

6 How to use active learning?

One way to address the system cold start problem is to use active learning to generate recommendations. Active learning is a technique that allows the recommender system to select the most informative ratings or feedback to collect from users, in order to improve its performance and reduce its uncertainty. Active learning can be applied by using different strategies, such as uncertainty sampling, diversity sampling, or expected utility sampling. Active learning can be used to bootstrap the recommender system, by acquiring ratings or feedback that can help it learn the user preferences or the item features faster and more accurately. Active learning can also be used to adapt the recommender system, by acquiring ratings or feedback that can help it cope with changes in user preferences or item features over time.

7 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?