Amlan Jyoti Das

Examples provide a multi-stage cluster component that performs a multi-stage clustering analysis on a plurality of items in a category associated with a selected item using a set of interrelationship factors. The multi-stage cluster component generates a cluster of non-substitute item-pairs, a cluster of traditional substitute item-pairs, and a cluster of variety item-pairs. The set of interrelationship factors includes at least one of measure of association, brand similarity, pack-size… Show more

Examples provide a multi-stage cluster component that performs a multi-stage clustering analysis on a plurality of items in a category associated with a selected item using a set of interrelationship factors. The multi-stage cluster component generates a cluster of non-substitute item-pairs, a cluster of traditional substitute item-pairs, and a cluster of variety item-pairs. The set of interrelationship factors includes at least one of measure of association, brand similarity, pack-size similarity, demographic similarity, item description similarity, lift, and/or percentage same-basket variable. A propensity score is generated for each item-pair. The propensity score is utilized to identify traditional substitute items and variety substitute items. Each substitute item is ranked based on the generated propensity score. The ranking is used to identify potential low-performance items for removal from inventory. Show less

Systems and methods for auto-naming nodes in a behavior tree are provided. An example method can include: providing a hierarchy of tree nodes by a computing device; generating a first corpus for each node at a final level; creating a first term-document matrix associated with the first corpus; identifying a first group of high-frequency words in the first term-document matrix; removing the first group of the high-frequency words obtain a second corpus; creating a second term-document matrix… Show more

Systems and methods for auto-naming nodes in a behavior tree are provided. An example method can include: providing a hierarchy of tree nodes by a computing device; generating a first corpus for each node at a final level; creating a first term-document matrix associated with the first corpus; identifying a first group of high-frequency words in the first term-document matrix; removing the first group of the high-frequency words obtain a second corpus; creating a second term-document matrix based on each of a set of predefined rules; identifying a second group of high-frequency words to represent node names; selecting a best set of the predefined rules based on an automatic evaluation model; generating a node name by removing a duplicate word in each node; incorporating feedback to generate a predicted name for each node; and selecting a final name for each node from the predicted name and the generated node name. Show less

Systems, methods, and computer-readable storage media for categorizing items based on attributes of the item and a shape of the item, where the shape of the item is determined from an image of the item. An exemplary system configured as disclosed herein can receive a request to categorize an item, the item having a plurality of attributes, and receive an image of the item. The system can identify, via a processor configured to perform image processing, a shape of the item based on the image… Show more

Systems, methods, and computer-readable storage media for categorizing items based on attributes of the item and a shape of the item, where the shape of the item is determined from an image of the item. An exemplary system configured as disclosed herein can receive a request to categorize an item, the item having a plurality of attributes, and receive an image of the item. The system can identify, via a processor configured to perform image processing, a shape of the item based on the image, and transform the plurality of attributes and the shape of the item, into a plurality of quantifiable values. The system can then categorize the item based on the quantifiable values. Show less

The system and method described herein enable generating inventory placement recommendations for a store based on transaction data. Inventory data associated with the individual store is obtained. The inventory data includes container location data, item location data, item category data, and transaction data. A store layout model of the store is generated based on the container location data and item location data. Item category relationships are calculated based on the transaction data and… Show more

The system and method described herein enable generating inventory placement recommendations for a store based on transaction data. Inventory data associated with the individual store is obtained. The inventory data includes container location data, item location data, item category data, and transaction data. A store layout model of the store is generated based on the container location data and item location data. Item category relationships are calculated based on the transaction data and the item category data. Inventory layouts are generated based on the store layout model and the calculated item category relationships. Average distance values for each inventory layout are calculated, and an inventory placement recommendation is generated based on the average distance values of the inventory layouts. The generated inventory placement recommendation enables arrangement of the inventory of the store based on past transactions to reduce the average travel distance of customers when shopping there. Show less

The efficacy of the generalized mean in controlling outliers is explored in this paper. We found that in the presence of outliers in the data, the generalized mean estimates the mean of the dominating population more accurately compared to the usual maximum likelihood estimator. Thus the generalized mean allows stable estimation of the target mean parameter without invoking the complications of sophisticated robust techniques. For example, while doing experimentation on the growth of species… Show more

The efficacy of the generalized mean in controlling outliers is explored in this paper. We found that in the presence of outliers in the data, the generalized mean estimates the mean of the dominating population more accurately compared to the usual maximum likelihood estimator. Thus the generalized mean allows stable estimation of the target mean parameter without invoking the complications of sophisticated robust techniques. For example, while doing experimentation on the growth of species, the data on the size or growth rate of a particular species are often contaminated with those from other species, where the behavior of the latter component is similar to that of a bunch of outlying observations. To carry out realistic growth related inference on the mean growth of the primary component in this situation, the generalized mean is recommended as a useful tool to the experimental biologists. There are innumerable other real-life scenarios where a suitably chosen generalized mean can provide better input in doing inference with real data compared to the arithmetic and other standard means. Show less

Examples provide demand transference modeling for item assortment management. A demand prediction component analyzes item attribute data using a demand transference model to calculate a magnitude of demand transfer between items in a set of substitute items associated with a proposed item assortment. The proposed item assortment includes at least one assortment change. The assortment change includes a set of one or more items to be added to a current item assortment and/or a set of one or more… Show more

Examples provide demand transference modeling for item assortment management. A demand prediction component analyzes item attribute data using a demand transference model to calculate a magnitude of demand transfer between items in a set of substitute items associated with a proposed item assortment. The proposed item assortment includes at least one assortment change. The assortment change includes a set of one or more items to be added to a current item assortment and/or a set of one or more items to be removed from the current item assortment. The demand prediction component generates a demand transference result including the calculated magnitude of demand transfer for each item in the set of substitute items and/or a predicted walk-off rate associated with lost demand. An assortment recommendation component generates an accept recommendation and/or a reject recommendation based on the demand transference result, the predicted walk-off rate, and/or a demand transference score. Show less