What are the limitations of collaborative filtering with context in recommender systems?

What are the limitations of collaborative filtering with context in recommender systems?

One limitation of collaborative filtering with context in recommender systems is the cold start problem. This occurs when there is insufficient data or information about a new user or item, making it difficult to accurately recommend relevant items. Another limitation is the sparsity of data, where there may be a lack of overlap or common preferences among users, resulting in limited or inaccurate recommendations. Additionally, collaborative filtering with context may struggle to handle dynamic or evolving user preferences, as it relies heavily on historical data. Finally, context-based collaborative filtering may face challenges in incorporating diverse contextual factors, such as time, location, or social influence, which can impact the accuracy and relevance of recommendations.