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A Top-N Recommender Model with Partially Predefined Structure

机译：具有部分预定义结构的Top-N推荐人模型

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摘要

Recommender systems can retrieve appropriate results based on users behavioral patterns and preferences. They may be built based on multi-label learning approaches, as each customer transaction may be labeled with several results that interest him/her. It is therefore useful to model the correlations between labels while controlling complexity of the learning algorithm. This paper presents a generative probabilistic model for online resources (products/URLs) recommendation, by capturing the complex local correspondence between the user's queries and the resources he/she has actually viewed. The structure of our model is partially defined and it is completed according to the observed data. Consequently, several links between observed and/or latent random variables are induced from the training dataset before starting the estimation of parameters. Experiments conducted on real data show the effectiveness of our approach.
机译：推荐系统可以根据用户的行为模式和偏好来检索适当的结果。它们可以基于多标签学习方法来构建，因为每个客户交易都可以标记有他/她感兴趣的几个结果。因此，在控制学习算法的复杂度的同时，对标签之间的相关性进行建模是有用的。本文通过捕获用户查询和他/她实际查看的资源之间的复杂本地对应关系，提出了一种在线资源（产品/ URL）推荐的生成概率模型。我们模型的结构是部分定义的，并根据观察到的数据完成。因此，在开始参数估计之前，会从训练数据集中引入观察到的和/或潜在随机变量之间的几个链接。在真实数据上进行的实验表明了我们方法的有效性。

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