Recommender Systems Questions
One limitation of collaborative filtering with trust is the cold start problem. This occurs when a new user or item enters the system and there is not enough trust data available to make accurate recommendations. Another limitation is the sparsity of trust data, as users may not have enough trust relationships with others to provide reliable recommendations. Additionally, collaborative filtering with trust can be susceptible to the "sybil attack," where malicious users create multiple fake identities to manipulate the trust ratings and influence recommendations. Lastly, the accuracy of recommendations can be affected by the homophily bias, where users tend to trust and be influenced by others who are similar to them, leading to limited diversity in recommendations.