By Katarina Athens-Miller, Anna Olecka & Jason Otte

Convolutional Sequence Embedding Recommendation Model (Caser) Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a near future’. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (\emph{Caser}) as a solution to address this requirement. The idea is to embed a sequence of recent items into an image’ in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public datasets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics. …

Every year I choose my favorite visualization projects. Looking back at old picks over the decade, a part of me longs for a simpler, more playful time when data graphics were used to tell jokes and show neat things.

Generate Alluvial Plots with a Single Line of Code (easyalluvial)Alluvial plots are similar to sankey diagrams and visualise categorical data over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping …

The selloff on Christmas eve was so bad it looked like a typical bear market capitulation. The following rally merely confirmed it.

Machine learning is a computational process. To that end, it is inextricably tied to computational power – the tangible material of chips and semiconductors that the algorithms of machine intelligence operate on. Most obviously, computational power and computing architectures shape the speed of training and inference in machine learning, and therefore influence the rate of progress in the technology. But, these relationships are more nuanced than that: hardware shapes the methods used by researchers and engineers in the design and development of machine learning models. Characteristics such as the power consumption of chips also define where and how machine learning can be used in the real world. Despite this, many analyses of the social impact of the current wave of progress in AI have not substantively brought the dimension of hardware into their accounts. While a common trope in both the popular press and scholarly literature is to highlight the massive increase in computational power that has enabled the recent breakthroughs in machine learning, the analysis frequently goes no further than this observation around magnitude. This paper aims to dig more deeply into the relationship between computational power and the development of machine learning. Specifically, it examines how changes in computing architectures, machine learning methodologies, and supply chains might influence the future of AI. In doing so, it seeks to trace a set of specific relationships between this underlying hardware layer and the broader social impacts and risks around AI. Computational Power and the Social Impact of Artificial Intelligence

Microsoft Excel could be the single most popular piece of software in the business community. Released over thirty years ago, Excel is still used every day in countries across the globe to store, manipulate, and analyze data. It’s so widely used that becoming an Excel wizard can be a significant boost to your earnings.

Sponsored Post.By Initiative for Analytics and Data Science Standards (IADSS)

Welcome to part III of debunking the optimism corrected bootstrap in high dimensions (quite high number of features) in the Christmas holidays. Previously we saw with a reproducible code implementation that this method is very bias when we have many features (50-100 or more). I suggest avoiding this method until at some point it has been reassessed thoroughly to find how bad this situation is with different numbers of dimensions. Yes, I know for some statisticians this is your favorite method and they tell people how their method is lacking in statistical power, but clearly this has much worse issues, at least on some data. People are currently using this method in genomic medicine, where we have high numbers of features low samples. Just re-run the code yourself and make up your own mind if in doubt. I have now 3 implementations (excluding Caret) confirming the bias. One written by me, two independent statisticians. Let’s run some more experiments.

The selloff on Christmas eve was so bad it looked like a typical bear market capitulation. The following rally merely confirmed it.