Recommender Systems: Algorithms, Evaluation and Limitations

Abstract

Aims/ objectives: This paper presents the different types of recommender filtering techniques. The main objective of the study is to provide a review of classical methods used in recommender systems such as collaborative filtering, content-based filtering and hybrid filtering, highlighting the main advantages and limitations. This paper also discusses the state-of-art machine learning based recommendation models including Clustering models and Bayesian Classifiers. Further, we discuss the widespread application of recommender systems to a variety of areas such as e-learning and e-news. Finally, the paper evaluates the performance of matrix factorization-based models, nearest neighbours algorithms and co-clustering algorithms in terms of different metrics.

Analysis of Precision Service of Agricultural Product e-Commerce Based on Multimodal Collaborative Filtering Algorithm

“…Since the recommender system needs to quickly process a large amount of user-item data to complete the nearest neighbor finding task, but with the increase of users and items, the calculation amount of the nearest neighbor increases sharply. At this time, the model adaptability of the recommender system becomes a constraint on its recommendation, one of the key factors of efficiency [22].…”