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- 2016
基于因子分解机的信任感知商品推荐
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Abstract:
摘要： 数据稀疏和运行速度慢是个性化推荐系统面临的难题。为了有效利用用户历史行为,基于用户的评分记录识别出用户感兴趣的内容,并结合用户间的信任关系,提出使用因子分解机(factorization machine, FM)模型进行评分预测。FM具有线性时间复杂度,并且对于稀疏的数据具有很好的学习能力,因而能进行快速推荐。试验结果表明,与传统方法相比,基于因子分解机的商品推荐方法的准确度有明显提高。
Abstract: The personalized recommender system suffers from sparse data and slow recommendation speed. A score prediction model based on Factorization Machine(FM)was proposed. The FM model utilizes users access history, identifies user-interested contents based on their scoring records and integrates trusts among different users. FM has a linear time complexity and excellent learning capability for sparse data, so it can quickly recommend. The results showed that the proposed FM model based on product recommendation approach was significantly more accurate than the traditional methods
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