How do you balance personalization and diversity in cold start recommendations?

1 Cold start problem

The cold start problem occurs when a recommender system has insufficient or no information about a user or an item to make accurate and reliable predictions. For example, when a new user joins a platform, the system does not know their preferences, tastes, or needs, and cannot recommend items that match them. Similarly, when a new item is added to the catalog, the system does not know its features, popularity, or relevance, and cannot recommend it to users who might like it. The cold start problem can reduce the user satisfaction, engagement, and retention, as well as the system performance, revenue, and growth.

2 Cold start types

There are three main types of cold start scenarios: user cold start, item cold start, and system cold start. User cold start refers to the situation where a new user enters the system and has not provided any feedback or ratings on any items. Item cold start refers to the situation where a new item is introduced to the system and has not received any feedback or ratings from any users. System cold start refers to the situation where a new system is launched and has no data on any users or items. Each type of cold start requires different strategies and techniques to overcome it.

3 Personalization and diversity

Personalization and diversity are two important aspects of recommender systems that can influence the user experience and satisfaction. Personalization means tailoring the recommendations to the individual user's preferences, needs, and context, while diversity means providing a variety of recommendations that cover different aspects, categories, or features of the items. Personalization and diversity can have trade-offs and conflicts, as too much personalization can lead to over-specialization and filter bubbles, while too much diversity can lead to irrelevance and noise.

4 Balancing personalization and diversity in user cold start

One way to balance personalization and diversity in user cold start is to use hybrid recommender systems that combine different methods and sources of information. For example, a hybrid system can use content-based filtering, which relies on the features of the items, and collaborative filtering, which relies on the feedback of other users, to generate recommendations. Content-based filtering can provide personalized recommendations based on the user's profile, while collaborative filtering can provide diverse recommendations based on the user's similarity to other users. Another way to balance personalization and diversity in user cold start is to use active learning, which involves asking the user to provide some feedback or ratings on a selected set of items. The system can choose the items that are both relevant and diverse, based on some criteria such as uncertainty, informativeness, or coverage.

5 Balancing personalization and diversity in item cold start

One way to balance personalization and diversity in item cold start is to use feature extraction and embedding techniques that can represent the items in a low-dimensional vector space. For example, a technique such as matrix factorization can decompose the user-item rating matrix into two matrices that capture the latent factors of the users and the items. The latent factors can then be used to compute the similarity or relevance of the new items to the existing users or items. Another way to balance personalization and diversity in item cold start is to use social and contextual information that can enrich the item description and provide additional cues for recommendation. For example, a system can use the metadata, tags, reviews, or ratings of the new items, or the temporal, spatial, or social context of the users or items, to generate recommendations that are both personalized and diverse.

6 Balancing personalization and diversity in system cold start

One way to balance personalization and diversity in system cold start is to use external data sources that can provide some initial information about the users or items. For example, a system can use demographic data, psychographic data, or social network data to create user profiles or segments, or use content data, semantic data, or domain knowledge to create item features or categories. The system can then use these data to generate recommendations that are both personalized and diverse. Another way to balance personalization and diversity in system cold start is to use online learning and feedback mechanisms that can update and improve the system over time. For example, a system can use bandit algorithms, reinforcement learning, or multi-armed bandits to explore and exploit different recommendation strategies, or use implicit or explicit feedback, such as clicks, purchases, ratings, or reviews, to learn from the user behavior and preferences.

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?