Black Friday is good time to pick up a robot vacuum for less than usual, especially considering these can be some of the most expensive smart home gadgets out there. Thankfully, you don't have to spend an arm and a leg to get a decent one. Right now, iRobot's affordable Roomba 694 is even cheaper than usual at $179, which is $95 off its normal price. Typically priced at $274, the Roomba 694 impressed us with its sleek design, good suction power that cleans carpeted and hard floors well and its ability to navigate around most household objects. There are more expensive machines that do a superior job sucking up pet hair, but the 694 was surprisingly good at it, too.

AI in retail industry is rapidly transforming the customer journey and experience. The fundamental steps of shopping have mostly stayed the same throughout the years: enter a store, choose the ideal item, and pay for it. With personalization, automation, and more efficiency, artificial intelligence has the ability to revamp the conventional shopping experience totally. The retail sector has been going through a digital shift for a while now. Every branch of retail companies has seen a rise in speed, efficiency, and accuracy, largely because of sophisticated data and predictive analytics technologies that support businesses in making data-driven business choices.

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Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequency of the user's interactions with the item. However, taking the PIF as an explicit feature incurs bias towards frequent items. Items that a user purchases frequently are assigned higher weights in the PIF-based recommender system and appear more frequently in the personalized recommendation list. As a result, the system will lose the fairness and balance between items that the user frequently purchases and items that the user never purchases. We refer to this systematic bias on personalized recommendation lists as frequency bias, which narrows users' browsing scope and reduces the system utility. We adopt causal inference theory to address this issue. Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram. In this paper, we propose a deconfounder model named FENDER (Frequency-aware Deconfounder for Next-basket Recommendation) to mitigate the frequency bias. With the deconfounder theory and the causal diagram we propose, FENDER decomposes PIF with a neural tensor layer to obtain substitute confounders for users and items. Then, FENDER performs unbiased recommendations considering the effect of these substitute confounders. Experimental results demonstrate that FENDER has derived diverse and fair results compared to ten baseline models on three datasets while achieving competitive performance. Further experiments illustrate how FENDER balances users' historical purchases and potential interests.

Retailers today are faced with mounting pressure to maximize their bottom lines while maintaining cost expectations for consumers. But amid volatile market changes, ever-evolving customer expectations and increased competition online, this is increasingly challenging to realize. Many retailers are turning to AI to help boost productivity while keeping costs in check. Research finds the global market size for AI in retail is expected to grow drastically, from $4.84 billion in 2021 to $31.18 billion in 2028. This sharp increase is attributed to surging demand for AI-powered retail solutions as the industry continues to undergo digital transformation.

Amazon SageMaker Data Wrangler helps you understand, aggregate, transform, and prepare data for machine learning (ML) from a single visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Data science practitioners generate, observe, and process data to solve business problems where they need to transform and extract features from datasets. Transforms such as ordinal encoding or one-hot encoding learn encodings on your dataset. These encoded outputs are referred as trained parameters.

Amazon has unveiled its newest delivery drone that will soon be dropping packages from 12 feet in the air in two U.S. cities. The retail giant has long wanted to solve the last leg of package delivery, especially since it launched Amazon Prime's Two-Day delivery offering in 2005. Jeff Bezos first announced drone delivery in 2013, but the service only made a single delivery three years after that. The drone, dubbed MK27-2, will start making deliveries in Lockeford, California, and College Station, Texas, by the end of 2022. The autonomous craft is about five-and-a-half feet in diameter, weighs 80 pounds and can only carry packages that weight less than five pounds.

Sequences are an important type of data that often occurs in fields such as medicine, business, finance, and education. The goal of sequential pattern mining is to discover frequently occurring sequences to extract useful knowledge from data. With the increase in the size of databases, mining long sequences is quite a challenging task. The sequence action set provides actions that are effective in sequence mining tasks for various data sets. In this post, we will show how the seqmc action is able to mine long sequences efficiently from a large database.

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We consider the revenue maximization problem for an online retailer who plans to display in order a set of products differing in their prices and qualities. Consumers have attention spans, i.e., the maximum number of products they are willing to view, and inspect the products sequentially before purchasing a product or leaving the platform empty-handed when the attention span gets exhausted. Our framework extends the well-known cascade model in two directions: the consumers have random attention spans instead of fixed ones, and the firm maximizes revenues instead of clicking probabilities. We show a nested structure of the optimal product ranking as a function of the attention span when the attention span is fixed. \sg{Using this fact, we develop an approximation algorithm when only the distribution of the attention spans is given. Under mild conditions, it achieves $1/e$ of the revenue of the clairvoyant case when the realized attention span is known. We also show that no algorithms can achieve more than 0.5 of the revenue of the same benchmark. The model and the algorithm can be generalized to the ranking problem when consumers make multiple purchases.} When the conditional purchase probabilities are not known and may depend on consumer and product features, we devise an online learning algorithm that achieves $\tilde{\mathcal{O}}(\sqrt{T})$ regret relative to the approximation algorithm, despite the censoring of information: the attention span of a customer who purchases an item is not observable. Numerical experiments demonstrate the outstanding performance of the approximation and online learning algorithms.