Cold Start Problem 편집하기 (부분)

==Approaches to Solving the Cold Start Problem==
Various strategies are employed to address the cold start problem in recommender systems:
*'''Hybrid Recommendation Systems''': Combine collaborative and content-based filtering, using item attributes to suggest items to new users or users with limited data.
*'''User Profiling''': Leverage demographic or explicit preference data provided by users upon registration to generate initial recommendations.
*'''Popular Item Recommendations''': Suggest popular or trending items to new users as a fallback until sufficient data is collected.
*'''Cross-Domain Recommendations''': Use data from related domains (e.g., movie preferences to recommend music) to make recommendations for new users.