Recommender Systems - Algorithms and Evaluation Metrics: Reviewing recommender system algorithms and evaluation metrics for assessing recommendation quality and user satisfaction
Keywords:
Recommender Systems, AlgorithmsAbstract
Recommender systems play a crucial role in modern information retrieval and e-commerce platforms by providing personalized recommendations to users. This paper provides a comprehensive review of recommender system algorithms and evaluation metrics. We first discuss the importance of recommender systems and their impact on user satisfaction and business performance. Then, we review the most widely used recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid approaches. Next, we delve into evaluation metrics used to assess the performance of recommender systems, such as precision, recall, and mean absolute error. We also discuss advanced evaluation techniques, including offline and online evaluation methods. Finally, we highlight challenges and future research directions in the field of recommender systems.
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