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Full-Text Articles in Business
Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
Research Collection School Of Computing and Information Systems
Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases). Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another supporting sequence …
How Artificial Intelligence Is Impacting Manufacturing Industry, Deepak Srinivasan, Maitreyi Ramesh Swaroop, Balaji Rajaram, Sri Krishan Iyer
How Artificial Intelligence Is Impacting Manufacturing Industry, Deepak Srinivasan, Maitreyi Ramesh Swaroop, Balaji Rajaram, Sri Krishan Iyer
Research Collection School Of Computing and Information Systems
In this survey, we study the impact of Artificial Intelligence (AI) on manufacturing sector. AI methods can be utilized to make new thoughts several ways: by delivering novel mixes of wellknown thoughts; by investigating the capability of theoretical spaces; and by making changes that empower the era of unexplored thoughts. AI will have less trouble in displaying the era of new thoughts than in automating their assessment. We describe the advances that have been made on AI in manufacturing industry. We close with how to overcome the issues in this area.