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本文介绍今年发表在ICML'21的工作,提出了一种计算推荐系统中用户嵌入表示(embedding)的新方法,有望被用来解决冷启动问题或在线场景中及时处理新用户。方法的核心思想如下:1)对数据集中一部分用户(例如点击数较多的用户)采用传统协同过滤得到嵌入表示;2)利用这些用户的嵌入表示的加权组合去计算其他用户(例如点击数较少的用户或全新的用户)的嵌入表示。这样做的目的是利用一部分well-trained的用户表示去间接计算另一部分few-shot或zero-shot的用户表示(直接计算容易过拟合),从而提升在少样本用户或新用户上的泛化性能。同时,这种表示方法可以实现inductive learning,即模型可以灵活的处理未来出现的新用户,不需要重新训练。

该方法的核心思想很简单,但有严格的理论保障,也在公开数据集上取得了优异的结果。核心思想可以被拓展到其他推荐系统的场景和方法上,也能被用到其他领域去处理一般化的实体表征学习的问题。

论文题目:Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach

论文链接:http://proceedings.mlr.press/v139/wu21j/wu21j.pdf

代码链接:https://github.com/qitianwu/IDCF

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Many clustering algorithms are guided by certain cost functions such as the widely-used $k$-means cost. These algorithms divide data points into clusters with often complicated boundaries, creating difficulties in explaining the clustering decision. In a recent work, Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020) introduced explainable clustering, where the cluster boundaries are axis-parallel hyperplanes and the clustering is obtained by applying a decision tree to the data. The central question here is: how much does the explainability constraint increase the value of the cost function? Given $d$-dimensional data points, we show an efficient algorithm that finds an explainable clustering whose $k$-means cost is at most $k^{1 - 2/d}\,\mathrm{poly}(d\log k)$ times the minimum cost achievable by a clustering without the explainability constraint, assuming $k,d\ge 2$. Taking the minimum of this bound and the $k\,\mathrm{polylog} (k)$ bound in independent work by Makarychev-Shan (ICML 2021), Gamlath-Jia-Polak-Svensson (2021), or Esfandiari-Mirrokni-Narayanan (2021), we get an improved bound of $k^{1 - 2/d}\,\mathrm{polylog}(k)$, which we show is optimal for every choice of $k,d\ge 2$ up to a poly-logarithmic factor in $k$. For $d = 2$ in particular, we show an $O(\log k\log\log k)$ bound, improving near-exponentially over the previous best bound of $O(k\log k)$ by Laber and Murtinho (ICML 2021).

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