Rug Plot
A rug plot is a compact way of illustrating the marginal distributions of a variable along x and y. Positions of the data points along x and y are denoted by tick marks, reminiscent of the tassels on a rug. Known Issues: Rug marks are overlaid onto the same axis as the original data. Changing the axis dimensions after calling rug will therefore cause the tick marks to become disassociated from the axes.
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Translational Recommender Networks
Representing relationships as translations in vector space lives at the heart of many neural embedding models such as word embeddings and knowledge graph embeddings. In this work, we study the connections of this translational principle with collaborative filtering algorithms. We propose Translational Recommender Networks (\textsc{TransRec}), a new attentive neural architecture that utilizes the translational principle to model the relationships between user and item pairs. Our model employs a neural attention mechanism over a \emph{Latent Relational Attentive Memory} (LRAM) module to learn the latent relations between user-item pairs that best explains the interaction. By exploiting adaptive user-item specific translations in vector space, our model also alleviates the geometric inflexibility problem of other metric learning algorithms while enabling greater modeling capability and fine-grained fitting of users and items in vector space. The proposed architecture not only demonstrates the state-of-the-art performance across multiple recommendation benchmarks but also boasts of improved interpretability. Qualitative studies over the LRAM module shows evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of \textsc{TransRec} to uncover hidden relational structure within implicit datasets. …

Strongly Hierarchical Factorization Machine
High-order parametric models that include terms for feature interactions are applied to various data min- ing tasks, where ground truth depends on interactions of features. However, with sparse data, the high- dimensional parameters for feature interactions often face three issues: expensive computation, difficulty in parameter estimation and lack of structure. Previous work has proposed approaches which can partially re- solve the three issues. In particular, models with fac- torized parameters (e.g. Factorization Machines) and sparse learning algorithms (e.g. FTRL-Proximal) can tackle the first two issues but fail to address the third. Regarding to unstructured parameters, constraints or complicated regularization terms are applied such that hierarchical structures can be imposed. However, these methods make the optimization problem more challeng- ing. In this work, we propose Strongly Hierarchical Factorization Machines and ANOVA kernel regression where all the three issues can be addressed without making the optimization problem more difficult. Ex- perimental results show the proposed models signifi- cantly outperform the state-of-the-art in two data min- ing tasks: cold-start user response time prediction and stock volatility prediction. …