In recent years, e-Commerce sales have risen and reached all-time highs. There is great significance in the order in which products are shown on e-Commerce websites. In this paper, we study a model in which a user is shown products sequentially and can either buy a product or proceed to the next one. We present an algorithm for ordering the products in such a way as to maximize the expected profit for the seller. We present the results of experiments conducted through the Mechanical Turk, which compares the order produced by our algorithm with other orders. The experiments show that our algorithm is significantly better than a naive ordering by price, and then a random order.

An increasingly important process of the internet age and the massive data era is file compression. One popular compression scheme, Lempel–Ziv–Welch (LZW), maintains a dictionary of previously seen strings. The dictionary is updated throughout the parsing process by adding new encountered substrings. Klein, Opalinsky and Shapira (2019) recently studied the option of selectively updating the LZW dictionary. They show that even inserting only a random subset of the strings into the dictionary does not adversely affect the compression ratio. Inspired by their approach, we propose a reinforcement learning based agent, RLZW, that decides when to add a string to the dictionary. The agent is first trained on a large set of data, and then tested on files it has not seen previously (i.e., the test set). We show that on some types of input data, RLZW outperforms the compression ratio of a standard LZW.

The QWERTY keyboard layout that is commonly used today was designed, over 100 years ago, for typewriters rather than for modern keyboards. Over the decades, many people have tried manually to come up with better layout designs. Recently, researchers have also attempted to automatically find a better keyboard layout by using advanced algorithms. In this paper we propose the use of deep learning with a genetic algorithm for finding improved keyboard layouts. We also show that using an appropriate crossover routine, instead of the crossover routine previously used in the literature, significantly improves the performance of the genetic algorithm. Our method, which we call MKLOGA, produces a keyboard layout that outperforms previous layouts, including those found by other algorithms, according to the realistic typing effort model of carpalx. We provide an installation of our keyboard layout. MKLOGA might also be useful for developing good layouts for languages other than English, and possibly for other domains in which objects must be placed in predefined locations.