Online Iterative Adaptive Dynamic Programming Approach for Solving the Zero-Sum Game for Nonlinear Continuous-Time Systems with Partially Unknown Dynamics

The current study presents an online iterative adaptive dynamic programming approach to resolve the zero-sum game (ZSG) for nonlinear continuous-time (CT) systems containing a partially unknown dynamic. The Hamilton-Jacobian-Issacs (HJI) equation is solved along the state trajectory according to the value function approximation and the policy...

Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling

Product bundling is a commonly-used marketing strategy in both offline retailers and online e-commerce systems. Current research on bundle recommendation is limited by: (1) noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session; and (2) specific tasks, holding unrealistic assumptions, e.g.,...

Recurrent knowledge graph embedding for effective recommendation

Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities...

CitRec 2017: International Workshop on Recommender Systems for Citizens

The "International Workshop on Recommender Systems for Citizens" (CitRec) is focused on a novel type of recommender systems both in terms of ownership and purpose: recommender systems run by citizens and serving society as a whole.

MRLR: Multi-level representation learning for personalized ranking in recommendation

Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem...

A computational design exploration platform supporting the formulation of design concepts

The comparison of various competing design concepts during conceptual architectural design is commonly needed for achieving a good final concept. For this, computational design exploration is a key approach. Unfortunately, most<br/>of existing research tends to skip this crucial process, and purely focuses on the late-stage design optimization...

Impacts of problem scale and sampling strategy on surrogate model accuracy: An application of surrogate-based optimization in building design

Surrogate-based Optimization is a useful approach when the objective function is computationally expensive to evaluate, compared to Simulation-based Optimization. In the surrogate-based method, analytically tractable “surrogate models” (also known as “Response Surface Models — RSMs” or “metamodels”), are constructed and validated for each...

Multi-objective and multidisciplinary design optimization of large sports building envelopes: A case study

Currently, in the conceptual envelope design of sports facilities, multiple engineering performance feedbacks (e.g. daylight, energy and structural performance) are expected to assist architectural design decision-making. In general, it is known as Building Performance Optimization in the conceptual architectural design phase. Essentially, it...