Recommender Systems Questions
One limitation of matrix factorization with context in recommender systems is the cold start problem. This refers to the difficulty of making accurate recommendations for new users or items that have limited or no historical data. Since matrix factorization relies on historical user-item interactions, it struggles to provide accurate recommendations in such scenarios.
Another limitation is the sparsity of data. In many recommender systems, the user-item interaction matrix is sparse, meaning that there are many missing entries. This sparsity makes it challenging for matrix factorization to accurately estimate the missing values and make reliable recommendations.
Additionally, matrix factorization with context may not effectively capture the temporal dynamics of user preferences. User preferences and item popularity can change over time, and matrix factorization alone may not adequately adapt to these changes without incorporating additional temporal information.
Furthermore, matrix factorization with context may struggle to handle the scalability of large datasets. As the number of users and items increases, the computational complexity of matrix factorization grows, making it computationally expensive and time-consuming.
Lastly, matrix factorization with context may not handle the diversity of user preferences and item characteristics well. It tends to focus on capturing the most dominant patterns and may overlook niche or personalized preferences, leading to less diverse and potentially biased recommendations.