ﻻ يوجد ملخص باللغة العربية
Significant development of communication technology over the past few years has motivated research in multi-modal summarization techniques. A majority of the previous works on multi-modal summarization focus on text and images. In this paper, we propose a novel extractive multi-objective optimization based model to produce a multi-modal summary containing text, images, and videos. Important objectives such as intra-modality salience, cross-modal redundancy and cross-modal similarity are optimized simultaneously in a multi-objective optimization framework to produce effective multi-modal output. The proposed model has been evaluated separately for different modalities, and has been found to perform better than state-of-the-art approaches.
Two-sided marketplaces are an important component of many existing Internet services like Airbnb and Amazon, which have both consumers (e.g. users) and producers (e.g. retailers). Traditionally, the recommendation system in these platforms mainly foc
This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entropy-based methods ignore trade-off among objectives or introduce unrel
The multi-objective optimization is to optimize several objective functions over a common feasible set. Since the objectives usually do not share a common optimizer, people often consider (weakly) Pareto points. This paper studies multi-objective opt
Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easil
Previous theory work on multi-objective evolutionary algorithms considers mostly easy problems that are composed of unimodal objectives. This paper takes a first step towards a deeper understanding of how evolutionary algorithms solve multi-modal mul