What are the limitations of content-based filtering?

The limitations of content-based filtering in recommender systems include:

1. Limited serendipity: Content-based filtering relies on the similarity between items, which can result in recommendations that are too similar to what the user has already seen or experienced. This can limit the discovery of new and diverse items.

2. Over-specialization: Content-based filtering tends to recommend items that are similar to the user's past preferences. This can lead to a narrow focus on a specific genre or type of item, potentially missing out on other relevant and interesting recommendations.

3. Cold start problem: Content-based filtering requires sufficient data about the user's preferences and item characteristics to make accurate recommendations. However, in the initial stages or for new users, there may not be enough data available, making it challenging to provide personalized recommendations.

4. Limited recommendation scope: Content-based filtering primarily focuses on item attributes and characteristics, such as genre, keywords, or content. It may not consider other important factors like social context, popularity, or user feedback, which can limit the overall recommendation quality.

5. Lack of diversity: Content-based filtering may not effectively capture the user's diverse interests or preferences. It tends to recommend items that are similar to the user's past choices, potentially overlooking other relevant and novel recommendations.

6. Limited ability to handle dynamic preferences: Content-based filtering assumes that the user's preferences remain relatively stable over time. However, user preferences can change, and content-based filtering may struggle to adapt to these changes, leading to less accurate recommendations.

7. Scalability: Content-based filtering can face challenges in handling large datasets and scaling to a large number of users and items. The computation and storage requirements can increase significantly as the number of items and users grow, making it less efficient for large-scale recommender systems.