What are the limitations of matrix factorization in recommender systems?

Some limitations of matrix factorization in recommender systems include:

1. Cold start problem: Matrix factorization requires a significant amount of user-item interaction data to accurately predict recommendations. However, in scenarios where there is limited or no data available for new users or items, matrix factorization struggles to provide accurate recommendations.

2. Sparsity: Recommender systems often deal with sparse matrices, where the majority of entries are missing. Matrix factorization struggles to handle such sparsity, as it may lead to inaccurate predictions and recommendations.

3. Scalability: Matrix factorization can become computationally expensive and time-consuming when dealing with large datasets. As the number of users and items increases, the matrix size grows, making it challenging to factorize efficiently.

4. Lack of interpretability: Matrix factorization models are often considered black-box models, as they do not provide explicit explanations for their recommendations. This lack of interpretability can be a limitation in scenarios where transparency and understanding of the recommendation process are crucial.

5. Cold start problem for new items: Similar to the cold start problem for new users, matrix factorization also struggles to provide accurate recommendations for new items that have limited or no interaction data. This limitation can hinder the ability to recommend novel or less popular items effectively.

6. Over-specialization and lack of diversity: Matrix factorization models tend to recommend items based on user preferences and past interactions. This can lead to over-specialization, where users are only recommended items similar to their previous choices, limiting the diversity of recommendations.

7. Data sparsity across user groups: Matrix factorization may face challenges when dealing with datasets that have imbalanced user-item interactions across different user groups. This can result in biased recommendations, as the model may not accurately capture the preferences and needs of underrepresented user groups.

It is important to note that these limitations can be addressed or mitigated to some extent through various techniques and advancements in recommender system research.