A common challenge is dealing with biased recommendations. Recommendations can often suffer from popularity bias, where popular items dominate the recommendations, and diversity bias, where the system recommends similar items. Techniques like diversity-aware recommendation algorithms or personalization can be used to mitigate these biases.

Another challenge is scalability, especially when dealing with large datasets and real-time recommendations. Techniques like matrix factorization or distributed systems can be employed to handle the scalability issue.

In addition, recommender systems need to handle the issue of data sparsity, where most users have only interacted with a few items. Techniques like matrix factorization methods or incorporating side information can help in addressing this problem.

One of the challenges in building recommender systems is the cold start problem, where new users or items have limited interaction data, making it difficult to provide accurate recommendations. This can be addressed by using content-based methods or using collaborative filtering techniques with similar users or items.

Privacy concerns are also a challenge in building recommender systems. Striking a balance between providing personalized recommendations and protecting user privacy is crucial. Techniques like differential privacy or user-controlled privacy settings can be utilized to address privacy concerns.