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CLiMF:Collaborative Less-Is-More Filtering

机译：CLiMF：协作少即是多过滤

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摘要

In this paper we tackle the problem of recommendation in the scenarios with binary relevance data,when only a few (k) items are recommended to individual users.Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets,or not specifically focused on improving top-k recommendations.To solve the problem we propose a new CF approach,Collaborative Less-is-More Filtering (CLiMF).In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR),which is a well-known information retrieval metric for capturing the performance of top-k recommendations.We achieve linear computational complexity by introducing a lower bound of the smoothed reciprocal rank metric.Experiments on two social network datasets show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods.
机译：本文针对二进制相关数据场景中的推荐问题，即仅向个别用户推荐几（k）个项目。关于协同过滤（CF）的过去工作未解决二进制相关数据集的排名问题为解决该问题，我们提出了一种新的CF方法，即协作式少经多过滤（CLiMF）。在CLiMF中，模型参数是通过直接最大化均值倒数排名（MRR）来学习的），这是一种捕获top-k建议的性能的著名信息检索指标。我们通过引入平滑倒数排名指标的下限来实现线性计算复杂性。两个社交网络数据集上的实验表明，CLiMF的性能明显优于a天真的基线和两种最新的CF方法。

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