From the Lab to Production: A Case Study of Session-Based Recommendations in the Home-Improvement Domain
E-commerce applications rely heavily on session-based recommendation algorithms to improve the shopping experience of their customers. Recent progress in session-based recommendation algorithms shows great promise. However, translating that promise to real-world outcomes is a challenging task for several reasons, but mostly due to the large number and varying characteristics of the available models. In this paper, we discuss the approach and lessons learned from the process of identifying and deploying a successful session-based recommendation algorithm for a leading e-commerce application in the home-improvement domain. To this end, we initially evaluate fourteen session-based recommendation algorithms in an offline setting using eight different popular evaluation metrics on three datasets.
Next-Paradigm Programming Languages: What Will They Look Like and What Changes Will They Bring?
What will be the common principles behind next-paradigm, high-productivity programming languages, and how will they change everyday program development? I would like to focus on a question with an answer that can be, surprisingly, clearer: what will be the common principles behind next-paradigm, high-productivity programming languages, and how will they change everyday program development?
Optimizing Training Data for Image Classifiers
In this paper, we propose a robust method for outlier removal to improve the performance for image classification. Increasing the size of training data does not necessarily raise prediction accuracy, due to instances that may be poor representatives of their respective classes.
Product Collection Recommendation in Online Retail
Recommender systems are an integral part of eCommerce services, helping to optimize revenue and user satisfaction. Bundle recommendation has recently gained attention by the research community since behavioral data supports that users often buy more than one product in a single transaction. In most cases, bundle recommendations are of the form “users who bought product A also bought products B, C, and D”. Although such recommendations can be useful, there is no guarantee that products A,B,C, and D may actually be related to each other. In this paper, we address the problem of collection recommendation, i.e., recommending a collection of products that share a common theme and can potentially be purchased together in a single transaction.
Algebraic Modeling in Datalog
Datalog is a deductive language tailored for easy database access. We introduce an algebraic modeling language in Datalog for mixed-integer linear optimization models.
Rk-means: Fast Clustering for Relational Data
This RelationalAI Research paper introduces Rk-means, or relationalk-means algorithm, for clustering relational data tuples without having to access the full data matrix.