What’s going on in my city? Recommender systems and electronic participatory budgeting

In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities – Cambridge, Miami and New York City–, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area

DRIVER Technology Watch Report

This report is part of the Discovery Workpackage (WP4) and is the third report out of four deliverables. The objective of this report is to give an overview of the latest technical developments in the world of digital repositories, digital libraries and beyond, in order to serve as theoretical and practical input for the technical DRIVER developments, especially those focused on enhanced publications. This report consists of two main parts, one part focuses on interoperability standards for enhanced publications, the other part consists of three subchapters, which give a landscape picture of current and surfacing technologies and communities crucial to DRIVER. These three subchapters contain the GRID, CRIS and LTP communities and technologies. Every chapter contains a theoretical explanation, followed by case studies and the outcomes and opportunities for DRIVER in this field

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

A Numerical and Experimental Study on Effect of Composition of Ar-N2 Shielding Gas on the Weld Bead Profile and its Prediction for Hot Wire Arc Additive Manufacturing

Wire arc additive manufacturing is a process of making three-dimensional metal parts in a layer-by-layer approach using a feed wire and electric arc as a heat source. Wire arc additive manufacturing (WAAM) is becoming more popular due to its ability to create complex 3D parts, less build time, high deposition rate, and significant cost savings. Out of the many parameters used in WAAM, one of the important parameters is shielding gas which plays a significant role in material quality, properties, and defects. In this study, a controlled amount of Argon (Ar) and Nitrogen (N2) shielding gases are used to see the effect on the weld bead depth and width. In addition, a computational fluid dynamics (CFD) model is used to perform numerical analysis. The data collected from the experiment is used to perform a regression analysis to predict future values. The amount of shielding gas mixture is controlled through a flowmeter to three different total flowrates. The result shows there is an increase in the depth and width of the weld bead with the increase in N2 percentage in the Ar-N2 shielding gas mixture. With the increase in Nitrogen percentage, the tungsten arc is observed unstable and spattering is noticed. The temperature of the surface of the base metal is increased while using the Ar-N2 mixture. The experiment result is further verified by developing and analyzing a three-dimensional computational fluid dynamics model using a volume of fluid (VOF) method. Support vector machine (SVM) regression model with Gaussian kernel function is used to perform the predictive regression analysis. Out of all the regression models, SVM has the lowest model loss for the collected data

NAIS: Neural Attentive Item Similarity Model for Recommendation

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM), our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems