CAJ | 학술논문

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.
대여하이용대규모은식반궤수거진행개성화추천진행료연구,제출료잠재요소모형 IFRM.해모형통과장추천임무전화위선택행위발생개솔적우화문제,극복료재은식반궤추천장경하지유정반궤이결핍부반궤도치적곤난.재차기출상,위료진일보제고효솔화가확전성,제출료병행화적은식반궤추천모형 p-IFRM.해모형통과장용호급산품수궤분통병중구우화경신서렬,체도료병행우화적목적.통과개솔추도,소제출적모형유견실적이론기출.통과재 MapReduce 병행계산광가하실현 p-IFRM,병재대규모진실수거집상진행실험,가이증명소제출적모형능구유효제고추천질량병차유량호적가확전성.
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.