The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Quality Web Information Retrieval: Towards Improving Semantic Recommender Systems with Friendsourcing

Web content quality is crucial in any domains, but it is even more critical in the health and e-learning ones. Users need to retrieve information that is precise, believable, and relevant to their problem. With the exponential growth of web contents, Recommender System has become indispensable for discovering quality information that might interest or be needed by web users. Quality-based Recommender Systems take into account quality criteria like credibility, believability, readability. In this paper, we present an approach to conceive Social Semantic Recommender Systems. In this approach a friendsourcing strategy is applied to better adequate recommendations to the user needs. The friendsourcing strategy focuses on the use of social force to assess quality of web content. In this paper we introduce the main research issues of this approach and detail the road-map we are following in the QHIR Project

Mobile recommender systems:Identifying the major concepts

© The Author(s) 2018. This article identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalised recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused mostly on the proposal of new algorithms that provide more accurate recommendations. However, the use of mobile devices and the rapid growth of the Internet and networking infrastructure have brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain

Towards persuasive social recommendation: knowledge model

[EN] The exponential growth of social networks makes fingerprint let by users on the Internet a great source of information, with data about their preferences, needs, goals, profile and social environment. These data are distributed across di↵erent sources of information (social networks, blogs, databases, etc.) that may contain inconsistencies and their accuracy is uncertain. Paradoxically, this unprecedented availability of heterogeneous data has meant that users have more information available than they actually are able to process and understand to extract useful knowledge from it. Therefore, new tools that help users in their decision-making processes within the network (e.g. which friends to contact with or which products to consume) are needed. In this paper, we show how we have used a graph-based model to extract and model data and transform it in valuable knowledge to develop a persuasive social recommendation system1.This work was partially supported by the project MINE-CO/FEDER TIN2012-365686-C03-01 of the Spanish government and by the Spanish Ministry of Education, Culture and Sports under the Program for R&D Valorisation and Joint Resources VLC/CAMPUS, as part of the Campus of International Excellence Program (Ref. SP20140788).Palanca Cámara, J.; Heras Barberá, SM.; Jorge Cano, J.; Julian Inglada, VJ. (2015). Towards persuasive social recommendation: knowledge model. ACM SIGAPP Applied Computing Review. 15(2):41-49. https://doi.org/10.1145/2815169.2815173S4149152Desel, J., Pernici, B., Weske, M. 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Billsus,Content-Based Recommendation Systems, ser. LNCS. Springer-Verlag, 2007, vol. 4321, pp. 325--341.J. Schafer, D. Frankowski, J. Herlocker, and S. Sen,Collaborative Filtering Recommender Systems, ser. LNCS. Springer, 2007, v. 4321, pp. 291--324.R. Burke, "Hybrid Recommender Systems: Survey and Experiments,"User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331--370, 2002.C. Chesñevar, A. Maguitman, and M. González,Empowering Recommendation Technologies Through Argumentation.Springer, 2009, pp. 403--422.G. Linden, J. Hong, M. Stonebraker, and M. Guzdial:, "Recommendation Algorithms, Online Privacy and More,"Comm. of the ACM, vol. 52, no. 5, 2009.Khare, Rohit and Çelik, Tantek, "Microformats: a pragmatic path to the semantic web" in15th international conference on World Wide Web, ACM, 2006, pp. 865--866.R. Fogués, J. M. Such, A. Espinosa, and A. Garcia-Fornes. 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Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl