An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments

Social recommendation has been popular and successful in various urban sustainable applications such as online sharing, products recommendation and shopping services. These applications allow users to form several implicit social networks through their daily social interactions. The users in such social networks can rate some interesting items and give comments. The majority of the existing studies have investigated the rating prediction and recommendation of items based on user-item bipartite graph and user-user social graph, so called social recommendation. However, the spatial factor was not considered in their recommendation mechanisms. With the rapid development of the service of location-based social networks, the spatial information gradually affects the quality and correlation of rating and recommendation of items. This paper proposes spatial social union (SSU), an approach of similarity measurement between two users that integrates the interconnection among users, items and locations. The SSU-aware location-sensitive recommendation algorithm is then devised. We evaluate and compare the proposed approach with the existing rating prediction and item recommendation algorithms subject to a real-life data set. Experimental results show that the proposed SSU-aware recommendation algorithm is more effective in recommending items with the better consideration of user's preference and location.This work was supported by the National Natural Science Foundation of China under Grant 61372187. G. Min’s work was partly supported by the EU FP7 CLIMBER project under Grant Agreement No. PIRSES-GA-2012-318939. L. T. Yang is the corresponding author

Social Relations and Methods in Recommender Systems: A Systematic Review

With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location. In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out. As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model. The most used metrics that we found, and the recommendation methods studied in mobile applications are presented. The information provided by this study can be valuable to increase the precision of the recommendations

Removing the Digital Divide for Senior Web Users

It is hard for the elderly to use the internet to find the resource they want. Usually help is needed for them to complete the task on the technology things. The main reason for this project is to research ideas on encourage senior people to make use of the web to locate helps they want, such as finding volunteers and professional helps. The scope of this project is to develop a new way of web access and content presentation methodologies that let senior people getting help from volunteers and various service providers more easily that incorporates social networking technology e.g. Facebook. By incorporating the social network web site like Facebook into the web application, senior people will be able to find volunteering help or other related service providers through social networking. Volunteers will show up in Google map in search results for senior to easily locate helps. Senior people can also search for self help videos tutorials through the web application search engine. A mobile version of the senior user application will also be developed for easy access on the road. Other features that benefit senior users includes voice input, control / content posting and collaborative social networking where a sponsors would sponsor a help task volunteer undertake

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

Culture and disaster risk management - synthesis of stakeholder attitudes during 3 Stakeholder Assemblies in Romania, Italy and Portugal

This report provides a synthesis of the results of three CARISMAND Stakeholder Assemblies held in A) Bucharest,Romania on April 14-15, 2016; B) Rome,Italy on February 27-28, 2017; and C) Lisbon,Portugal on February 27-28, 2018. These Stakeholder Assemblies, together with six Citizen Summits (see Deliverables D5.3 – D5.9) were part of the CARISMAND cycle of events (see Figure 1 below). This cycle of events was the key concept at the core of the CARISMAND project which aimed to ensure a comprehensive feedback loop betweendisaster practitioners and citizens. It also allowed for the progression of ideas co-created by disaster practitioners and citizens. The locations of the three Stakeholder Assemblies were chosen due to their rather different “backgrounds”. The three countries had been struck at the time of the respective event by different types of disasters. In addition, the three countries have very different “cultures”, or cultural impacts, at a societal level. Romania has a comparatively strong authoritative systems due to its political history; Italy has experienced a strong direct in-flow of migrants in the last years due to its geological location; and Portugal has long been a traditional “melting pot” where, over more than a millennium, people from different cultural backgrounds and ethnic origins (in particular North Africa, South America, and Europe) have lived together. Accordingly, these differences were expected to allow a wide range of practitioners’ attitudes and perceptions related to cultural factors in disaster management to emerge. In order to not only gather a variety of attitudes and perceptions but also promote cross-sectional knowledge transfer, the audience in all three events consisted of a wide range of practitioners who are typically involved in disaster management, e.g., civil protection agencies , the emergency services, paramedics, nurses, environmental protection agencies, the Red Cross, firefighters, the military, and the police. Further, these practitioners were from several regions in the respective country; in Portugal, the Stakeholder Assembly also included practitioners from the island of Madeira. The 40-60 participants per event were recruited via invitations sent to various organisations and institutions that play a role in disaster management, and via direct contacts of local partners in the CARISMAND consortium. Each assembly consisted of a mix of presentations and discussion groups to combine dissemination with information gathering (for detailed schedules see Appendices A1-A3). In an initial general assembly, the event started with presentations of the CARISMAND project and its main goals and concepts. Then, participants were split into small working groups in separate breakout rooms, where they discussed and provided feedback on a specific topic. After each working group session, panel discussions allowed the participants to present the results of their working group to the rest of the audience. After each panel discussion, keynote speakers gave presentations related to the topic that had been discussed during the working groups. This schedule was designed to ensure that participants are provided with detailed information about recent developments in disaster management, but without influencing the attitudes and perceptions expressed in the working groups. In the third Stakeholder Assembly, different sets of recommendations for practitioners (related to the use of cultural factors in disaster management) were presented to the general audience, followed by small discussion group sessions as described above.The project was co-funded by the European Commission within the Horizon2020 Programme (2014–2020).peer-reviewe

Recommendations based on social links

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research

Topological Influence-Aware Recommendation on Social Networks

Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence.</jats:p