Effectiveness of the data generated on different time in latent factor model

Abstract

User selection data accumulates as time goes by. Although the recent selections are usually assumed to have higher impact on the recommendation accuracy, empirical studies on this problem are limited. For old data, whether they can contribute to the recommendation accuracy is still to be determined. On one hand, changes in short-term user preference over time may limit their effectiveness in prediction, but on the other hand, one cannot rule out their potential in capturing long term user preferences. The result is important for the system owner to determine which data is useful to make the recommendation accurately. While there have been some related studies on the time dependency of data quality using neighbor-based CF methods (e.g., [4]), its effects remain unverified for other CF methods. In this paper, we study the effect of data generated over different time period on recommendation precision using several popular model-based CF algorithms (latent factor models). Experiment results show that while more recent data expectedly have larger impacts, the usefulness of older data cannot be ignored as long as there are sufficient old samples. However, the addition of insufficient amount of old data seems to have negative impacts.

Influence of time on user profiling and recommending researchers in social media

“…In contrast, there is the opposite effect for user-generated content, i .e., involving older consumption data has a negative influence on the recommender accuracy. Zheng et al [17] also evaluated the effect of data generated over a different time period on recommendation precision using several popular model-based collaborative filtering algorithms. Their results show that while more recent data have larger impacts, the usefulness of older data cannot be ignored as long as there are sufficient old samples.…”