Abstract
Collaborative filtering is the type of algorithm that has the most variants and is currently the most widely used in recommender systems. The advantage is that it does not require much domain knowledge, and can help to get better recommendation by machine learning. In this paper, a time-aware user&item-based collaborative filtering algorithm and a time-aware latent factor model recommendation algorithm are proposed. Because the two types of algorithms reflect the time dynamics in different ways, the effects are also different. We then put forward an integrated time-aware collaborative filtering algorithm ITCF by integrating them together to compensate for each other's shortcomings. The experimental results show that the proposed two single time-aware algorithms can both obtain good recommendation effect, and the integrated algorithm ITCF gets higher accuracy than the single models with time factors.
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Wan, Y., Chen, Y., Yan, C. (2021). An Integrated Time-Aware Collaborative Filtering Algorithm. In: Uden, L., Ting, IH., Wang, K. (eds) Knowledge Management in Organizations. KMO 2021. Communications in Computer and Information Science, vol 1438. Springer, Cham. https://doi.org/10.1007/978-3-030-81635-3_30
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DOI: https://doi.org/10.1007/978-3-030-81635-3_30
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