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A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

机译：一种用于个人和组用户智能推荐系统的时间感知混合方法

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摘要

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.
机译：虽然最近个人和集团推荐系统已经迅速发展，但仍存在挑战和局限性。特别是，用户在整个时间内不断探索新项目并更改他们的偏好，这导致建立准确的用户简档并提供精确的推荐结果。在此上下文中，本研究解决了用户偏好的时间意识，并提出了一个单独的和组建议的混合推荐方法，以更好地满足用户偏好变化，从而提高推荐性能。实验结果表明，在个人和群体建议中，所提出的方法在精度，召回，新奇和多样性方面优于几个基线算法。此外，很明显，通过捕获研究中的用户偏好变化，可以很大程度上提高推荐性能。这些发现有利于增加对用户动态偏好变化的理解，以建立更精确的用户简档并扩大开发更有效和高效的推荐系统的知识。

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