Fast and Accurate Evaluation of Collaborative Filtering Recommendation Algorithms

Abstract

Collaborative filtering are recommender systems algorithms that provide personalized recommendations to users in various online environments such as movies, music, books, jokes and others. There are many such recommendation algorithms and, regarding experimental evaluations to find which algorithm performs better a lengthy process needs to take place and the time required depends on the size of the dataset and the evaluation metrics used. In this paper we present a novel method that is based on a series of steps that include random subset selections, ensemble learning and the use of well-known evaluation metrics Mean Absolute Error and Precision to identify, in a fast and accurate way, which algorithm performs the best for a given dataset. The proposed method has been experimentally evaluated using two publicly available datasets with the experimental results showing that the time required for the evaluation is significantly reduced, while the results are accurate when compared to a full evaluation cycle.

Soft Computing, Artificial Intelligence and Applications

“…(c) Hybrid algorithms [30]: These algorithms combine the advantages of memory-based and model-based algorithms or use additional information or techniques to improve the performance of collaborative filtering. For example, some hybrid algorithms can use content-based features or metadata to augment the feedback matrix or use ensemble methods or deep learning models to integrate multiple collaborative filtering models.…”

Revealing Sustainable Growth for Fitbit: A Data-driven Marketing Approach based on K-Means Clustering and Collaborative Filtering

“…(c) Hybrid algorithms [30]: These algorithms combine the advantages of memory-based and model-based algorithms or use additional information or techniques to improve the performance of collaborative filtering. For example, some hybrid algorithms can use content-based features or metadata to augment the feedback matrix or use ensemble methods or deep learning models to integrate multiple collaborative filtering models.…”