基于大规模隐式反馈的个性化推荐

摘要

This paper explores the area of personalized recommendation based on large-scale implicit feedback, where only positive feedback is available. To tackle the difficulty arising from lack of negative samples, a novel latent factor model IFRM is proposed, to convert the recommendation task into adoption probability optimization problem. To further improve efficiency and scalability, a parallel version of IFRM named p-IFRM is presented. By randomly partitioning users and items into buckets and thus reconstructing update sequence, IFRM can be learnt in parallel. The study theoretically derives the model from Bayesian analysis and experimentally demonstrates its effectiveness and efficiency by implementing p-IFRM under MapReduce framework and conducting comprehensive experiments on real world large datasets. The experiment results show that the model improves recommendation quality and performs well in scalability.%对如何利用大规模隐式反馈数据进行个性化推荐进行了研究,提出了潜在要素模型 IFRM.该模型通过将推荐任务转化为选择行为发生概率的优化问题,克服了在隐式反馈推荐场景下只有正反馈而缺乏负反馈导致的困难.在此基础上,为了进一步提高效率和可扩展性,提出了并行化的隐式反馈推荐模型 p-IFRM.该模型通过将用户及产品随机分桶并重构优化更新序列,达到了并行优化的目的.通过概率推导,所提出的模型有坚实的理论基础.通过在 MapReduce 并行计算框架下实现 p-IFRM,并在大规模真实数据集上进行实验,可以证明所提出的模型能够有效提高推荐质量并且有良好的可扩展性.