Human in the Loop Enrichment of Product Graphs with Probabilistic Soft Logic
Wednesday, January 1, 2020
Product graphs have emerged as a powerful tool for online retailers to enhance product semantic search, catalog navigation, and recommendations. Their versatility stems from the fact that they can uniformly store and represent different relationships between products, their attributes, concepts or abstractions etc, in an actionable form.
Learning Models over Relational Data using Sparse Tensors and Functional Dependencies
Wednesday, January 1, 2020
Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations; export the training dataset defined by such queries; convert this dataset into the format of an external learning tool; and train the desired model using this tool.
A Layered Aggregate Engine for Analytics Workloads
Wednesday, January 2, 2019
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.
A Layered Aggregate Engine for Analytics Workloads
Wednesday, January 2, 2019
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.
Strictly Declarative Specification of Sophisticated Points-to Analyses
Wednesday, January 2, 2019
We present the DOOP framework for points-to analysis of Java programs. DOOP builds on the idea of specifying pointer analysis algorithms declaratively, using Datalog: a logic-based language for defining (recursive) relations. We carry the declarative approach further than past work by describing the full end-to-end analysis in Datalog and optimizing aggressively using a novel technique specifically targeting highly recursive Datalog programs.
Counting Triangles under Updates in Worst-Case Optimal Time
Tuesday, January 1, 2019
We consider the problem of incrementally maintaining the triangle count query under single-tuple updates to the input relations. We introduce an approach that exhibits a space-time tradeoff such that the space-time product is quadratic in the size of the input database and the update time can be as low as the square root of this size.