Recommender systems are one of the most im- portant technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative fil...Recommender systems are one of the most im- portant technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative filter- ing CF family, have problems ness. These problems hinder such as scalability and sparse- further developments of rec- ommender systems. We propose a new recommendation al- gorithm based on item quality and user rating preferences, which can significantly decrease the computing complexity. Besides, it is interpretable and works better when the data is sparse. Through extensive experiments on three benchmark data sets, we show that our algorithm achieves higher accu- racy in rating prediction compared with the traditional ap- proaches. Furthermore, the results also demonstrate that the problem of rating prediction depends strongly on item quality and user rating preferences, thus opens new paths for further study.展开更多
文摘Recommender systems are one of the most im- portant technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative filter- ing CF family, have problems ness. These problems hinder such as scalability and sparse- further developments of rec- ommender systems. We propose a new recommendation al- gorithm based on item quality and user rating preferences, which can significantly decrease the computing complexity. Besides, it is interpretable and works better when the data is sparse. Through extensive experiments on three benchmark data sets, we show that our algorithm achieves higher accu- racy in rating prediction compared with the traditional ap- proaches. Furthermore, the results also demonstrate that the problem of rating prediction depends strongly on item quality and user rating preferences, thus opens new paths for further study.
