A comparative study between collaborative filtering and content-based recommendation
Abstract
The field of recommendation systems has seen a significant amount of research and development over the years. Two popular approaches in this area are collaborative filtering and content-based recommendation. Collaborative filtering utilizes the preferences and behaviors of users to suggest items they might like based on their similarity to other users. In contrast, content-based recommendation focuses on the properties of the items themselves and suggests items with similar characteristics to those previously preferred by a user. This similar review means to assess the viability and proficiency of both cooperative separating and content-based suggestion draws near. Based on their accuracy in recommending items, coverage of the recommendation space, and scalability in terms of the number of users and items, the two approaches are the focus of the study. The study reveals that content-based recommendation and collaborative filtering both have advantages and disadvantages. Because it requires a significant amount of data on user-item interactions to provide meaningful recommendations, collaborative filtering performs better than content-based recommendation in terms of accuracy but fails to provide adequate coverage. On the other hand, content-based recommendation performs well in terms of coverage but is constrained by the variety of item properties and user interests. Overall, this study provides insights into the trade-offs between collaborative approaches and highlights the importance of selecting the appropriate approach based on the context and goals.
Keywords
Collaborative filtering, Content-based recommendation, Machine learning, Cosine similarity, Mean absolute error, Root mean squared error, Statistical analysis.