Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Tag-based Recommender System for Context-Aware Content Dissemination in Opportunistic Networks

Content dissemination in opportunistic networks is a hot research topic that attracted a lot of interest in the last few years. The key idea is to optimise the diffusion of content among nodes in opportunistic networks to ensure that users are always able to obtain the most relevant items according to their interests. The classical approach is to statically define a set of interests for each user, and make sure that they receive items matching those interests. In this paper, we propose a novel approach, based on the dynamic and automatic identification of interests. To do so, we exploit the tags that users assign to the items they create, and the tags of the items that they download. We model these actions through a folksonomy and the related tripartite graph, with different nodes for users, items, and tags. We use this graph as the basis for identifying the relevance of the items. Specifically, we use a tag-based recommender system on the graph, called PLIERS, that is able to calculate the relevance of an item for a certain user, with respect to the items that are already linked to this user. We validate our approach through a series of simulations. We emulate the presence of a variable number of agents which randomly move, create and tag items, and possibly encounter other agents. Each agent maintains a tripartite graph locally, representing its actions, and it integrates this graph with information received from other encountered nodes. The agents use PLIERS on their local graph to assess the relevance of the items they find, and they decide whether these items are relevant for them or not. We evaluate the accuracy of the results by comparing the recommendations on the local graphs with the relevance of the items (calculated through PLIERS) on a global graph obtained by merging together all the local graphs of the nodes. This graph represents the complete knowledge of all actions in the network and it allows us to obtain the best possible recommendations for a target user, that could be obtained if all the nodes had the full knowledge of the actions of other nodes. The results indicate that the recommendations on the local graph are accurate and that the local knowledge of nodes reaches the global knowledge in the network through a sufficiently high number of contacts

Mobile app recommendations using deep learning and big data

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMRecommender systems were first introduced to solve information overload problems in enterprises. Over the last decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media and mobile app stores. Several methods have been proposed over the years to build recommender systems. The most popular approaches are based on collaborative filtering techniques, which leverage the similarities between consumer tastes. But the current state of the art in recommender systems is deep-learning methods, which can leverage not only item consumption data but also content, context, and user attributes. Mobile app stores generate data with Big Data properties from app consumption data, behavioral, geographic, demographic, social network and user-generated content data, which includes reviews, comments and search queries. In this dissertation, we propose a deep-learning architecture for recommender systems in mobile app stores that leverage most of these data sources. We analyze three issues related to the impact of the data sources, the impact of embedding layer pretraining and the efficiency of using Kernel methods to improve app scoring at a Big Data scale. An experiment is conducted on a Portuguese Android app store. Results suggest that models can be improved by combining structured and unstructured data. The results also suggest that embedding layer pretraining is essential to obtain good results. Some evidence is provided showing that Kernel-based methods might not be efficient when deployed in Big Data contexts

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Undergraduate and Graduate Course Descriptions, 2023 Spring

Wright State University undergraduate and graduate course descriptions from Spring 2023

The drivers of Corporate Social Responsibility in the supply chain. A case study.

Purpose: The paper studies the way in which a SME integrates CSR into its corporate strategy, the practices it puts in place and how its CSR strategies reflect on its suppliers and customers relations. Methodology/Research limitations: A qualitative case study methodology is used. The use of a single case study limits the generalizing capacity of these findings. Findings: The entrepreneur’s ethical beliefs and value system play a fundamental role in shaping sustainable corporate strategy. Furthermore, the type of competitive strategy selected based on innovation, quality and responsibility clearly emerges both in terms of well defined management procedures and supply chain relations as a whole aimed at involving partners in the process of sustainable innovation. Originality/value: The paper presents a SME that has devised an original innovative business model. The study pivots on the issues of innovation and eco-sustainability in a context of drivers for CRS and business ethics. These values are considered fundamental at International level; the United Nations has declared 2011 the “International Year of Forestry”

Planetary Science Vision 2050 Workshop : February 27–28 and March 1, 2017, Washington, DC

This workshop is meant to provide NASA’s Planetary Science Division with a very long-range vision of what planetary science may look like in the future.Organizer, Lunar and Planetary Institute ; Conveners, James Green, NASA Planetary Science Division, Doris Daou, NASA Planetary Science Division ; Science Organizing Committee, Stephen Mackwell, Universities Space Research Association [and 14 others]PARTIAL CONTENTS: Exploration Missions to the Kuiper Belt and Oort Cloud--Future Mercury Exploration: Unique Science Opportunities from Our Solar System’s Innermost Planet--A Vision for Ice Giant Exploration--BAOBAB (Big and Outrageously Bold Asteroid Belt) Project--Asteroid Studies: A 35-Year Forecast--Sampling the Solar System: The Next Level of Understanding--A Ground Truth-Based Approach to Future Solar System Origins Research--Isotope Geochemistry for Comparative Planetology of Exoplanets--The Moon as a Laboratory for Biological Contamination Research--“Be Careful What You Wish For:” The Scientific, Practical, and Cultural Implications of Discovering Life in Our Solar System--The Importance of Particle Induced X-Ray Emission (PIXE) Analysis and Imaging to the Search for Life on the Ocean Worlds--Follow the (Outer Solar System) Water: Program Options to Explore Ocean Worlds--Analogies Among Current and Future Life Detection Missions and the Pharmaceutical/ Biomedical Industries--On Neuromorphic Architectures for Efficient, Robust, and Adaptable Autonomy in Life Detection and Other Deep Space Missions

1981-82, 1982-83 GENERAL ISSUE- BULLETIN

1981-82, 1982-83 GENERAL ISSUE- BULLETINhttps://digitalrepository.unm.edu/course_catalogs/1090/thumbnail.jp