A Dynamic Trust Relations-Based Friend Recommendation Algorithm in Social Network Systems

A discovered algorithm based on the dynamic trust relations of users in a social network system (SNS) was proposed aiming at getting useful information more efficiently in an SNS. The proposed dynamic model combined the interests and trust relations of users to explore their good friends for recommendations. First, the network based on the interests and trust relations of users was set up. Second, the temporal factor was added to the model, then a dynamic model of the degree of the interest and trust relations of the users was calculated. Lastly, the similarities among the users were measured via this dynamic model, and the recommendation list of good friends was achieved. Results showed that the proposed algorithm effectively described the changes in the interest similarities and trust relations of users with time, and the recommended result was more accurate and effective than the traditional ones

Open challenges in relationship-based privacy mechanisms for social network services

[EN] Social networking services (SNSs) such as Facebook or Twitter have experienced an explosive growth during the few past years. Millions of users have created their profiles on these services because they experience great benefits in terms of friendship. SNSs can help people to maintain their friendships, organize their social lives, start new friendships, or meet others that share their hobbies and interests. However, all these benefits can be eclipsed by the privacy hazards that affect people in SNSs. People expose intimate information of their lives on SNSs, and this information affects the way others think about them. It is crucial that users be able to control how their information is distributed through the SNSs and decide who can access it. This paper presents a list of privacy threats that can affect SNS users, and what requirements privacy mechanisms should fulfill to prevent this threats. Then, we review current approaches and analyze to what extent they cover the requirementsThis article has been developed as a result of a mobility stay funded by the Erasmus Mundus Programme of the European Comission under the Transatlantic Partnership for Excellence in Engineering-TEE Project.López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2015). Open challenges in relationship-based privacy mechanisms for social network services. International Journal of Human-Computer Interaction. 31(5):350-370. doi:10.1080/10447318.2014.1001300S35037031

Three Essays on Friend Recommendation Systems for Online Social Networks

Social networking sites (SNSs) first appeared in the mid-90s. In recent years, however, Web 2.0 technologies have made modern SNSs increasingly popular and easier to use, and social networking has expanded explosively across the web. This brought a massive number of new users. Two of the most popular SNSs, Facebook and Twitter, have reached one billion users and exceeded half billion users, respectively. Too many new users may cause the cold start problem. Users sign up on a SNS and discover they do not have any friends. Normally, SNSs solve this problem by recommending potential friends. The current major methods for friend recommendations are profile matching and “friends-of-friends.” The profile matching method compares two users’ profiles. This is relatively inflexible because it ignores the changing nature of users. It also requires complete profiles. The friends-of-friends method can only find people who are likely to be previously known to each other and neglects many users who share the same interests. To the best of my knowledge, existing research has not proposed guidelines for building a better recommendation system based on context information (location information) and user-generated content (UGC). This dissertation consists of three essays. The first essay focuses on location information and then develops a framework for using location to recommend friends--a framework that is not limited to making only known people recommendations but that also adds stranger recommendations. The second essay employs UGC by developing a text analytic framework that discovers users’ interests and personalities and uses this information to recommend friends. The third essay discusses friend recommendations in a certain type of online community – health and fitness social networking sites, physical activities and health status become more important factors in this case. Essay 1: Location-sensitive Friend Recommendations in Online Social Networks GPS-embedded smart devices and wearable devices such as smart phones, tablets, smart watches, etc., have significantly increased in recent years. Because of them, users can record their location at anytime and anyplace. SNSs such as Foursquare, Facebook, and Twitter all have developed their own location-based services to collect users’ location check-in data and provide location-sensitive services such as location-based promotions. None of these sites, however, have used location information to make friend recommendations. In this essay, we investigate a new model to make friend recommendations. This model includes location check-in data as predictors and calculates users’ check-in histories--users’ life patterns--to make friend recommendations. The results of our experiment show that this novel model provides better performance in making friend recommendations. Essay 2: Novel Friend Recommendations Based on User-generated Contents More and more users have joined and contributed to SNSs. Users share stories of their daily life (such as having delicious food, enjoying shopping, traveling, hanging out, etc.) and leave comments. This huge amount of UGC could provide rich data for building an accurate, adaptable, effective, and extensible user model that reflects users’ interests, their sentiments about different type of locations, and their personalities. From the computer-supported social matching process, these attributes could influence friend matches. Unfortunately, none of the previous studies in this area have focused on using these extracted meta-text features for friend recommendation systems. In this study, we develop a text analytic framework and apply it to UGCs on SNSs. By extracting interests and personality features from UGCs, we can make text-based friend recommendations. The results of our experiment show that text features could further improve recommendation performance. Essay 3: Friend Recommendations in Health/Fitness Social Networking Sites Thanks to the growing number of wearable devices, online health/fitness communities are becoming more and more popular. This type of social networking sites offers individuals the opportunity to monitor their diet process and motivating them to change their lifestyles. Users can improve their physical activity level and health status by receiving information, advice and supports from their friends in the social networks. Many studies have confirmed that social network structure and the degree of homophily in a network will affect how health behavior and innovations are spread. However, very few studies have focused on the opposite, the impact from users’ daily activities for building friendships in a health/fitness social networking site. In this study, we track and collect users’ daily activities from Record, a famous online fitness social networking sites. By building an analytic framework, we test and evaluate how people’s daily activities could help friend recommendations. The results of our experiment have shown that by using the helps from these information, friend recommendation systems become more accurate and more precise

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

Enhancing Privacy Management on Social Network Services

Tesis por compendioIn the recent years, social network services, such as Facebook or LinkedIn, have experienced an exponential growth. People enjoy their functionalities, such as sharing photos, finding friends, looking for jobs, and in general, they appreciate the social benefits that social networks provide. However, as using social network has become routine for many people, privacy breaches that may occur in social network services have increased users' concerns. For example, it is easy to find news about people being fired because of something they shared on a social network. To enable people define their privacy settings, service providers employ simple access controls which usually rely exclusively on lists or circles of friends. Although these access controls are easy to configure by average users, research literature points out that they are lacking elements, such as tie strength, that play a key role when users decide what to share and with whom. Additionally, despite the simplicity of current access controls, research on privacy on social media reports that people still struggle to effectively control how their information flows on these services. To provide users with a more robust privacy framework, related literature proposes a new paradigm for access controls based on relationships. In contrast to traditional access controls where permissions are granted based on users and their roles, this paradigm employs social elements such as the relationship between the information owner and potential viewers (e.g., only my siblings can see this photo). Access controls that follow this paradigm provide users with mechanisms for disclosure control that represent more naturally how humans reason about privacy. Furthermore, these access controls can deal with specific issues that social network services present. Specifically, users often share information that concerns many people, especially other members of the social network. In such situations, two or more people can have conflicting privacy preferences; thus, an appropriate sharing policy may not be apparent. These situations are usually identified as multiuser privacy scenarios. Since relationship based access controls are complex for the average social network user, service providers have not adopted them. Therefore, to enable the implementation of such access controls in current social networks, tools and mechanisms that facilitate their use must be provided. To that aim, this thesis makes five contributions: (1) a review of related research on privacy management on social networks that identifies pressing challenges in the field, (2) BFF, a tool for eliciting automatically tie strength and user communities, (3) a new access control that employs communities, individual identifiers, tie strength, and content tags, (4) a novel model for representing and reasoning about multiuser privacy scenarios, employing three types of features: contextual factors, user preferences, and user arguments; and, (5) Muppet, a tool that recommends sharing policies in multiuser privacy scenarios.En los últimos años, los servicios de redes sociales, como Facebook o LinkedIn, han experimentado un crecimiento exponencial. Los usuarios valoran positivamente sus muchas funcionalidades tales como compartir fotos, o búsqueda de amigos y trabajo. En general, los usuarios aprecian los beneficios que las redes sociales les aportan. Sin embargo, mientras el uso de redes sociales se ha convertido en rutina para mucha gente, brechas de privacidad que pueden ocurrir en redes sociales han aumentado los recelos de los usuarios. Por ejemplo, es sencillo encontrar en las noticias casos sobre personas que han perdido su empleo debido a algo que compartieron en una red social. Para facilitar la definición de los ajustes de privacidad, los proveedores de servicios emplean controles de acceso sencillos que normalmente se basan, de forma exclusiva, en listas o círculos de amigos. Aunque estos controles de acceso son fáciles de configurar por un usuario medio, investigaciones recientes indican que éstos carecen de elementos tales como la intensidad de los vínculos personales, que juegan un papel clave en cómo los usuarios deciden qué compartir y con quién. Además, a pesar de la simplicidad de los controles de acceso, investigaciones sobre privacidad en redes sociales señalan que los usuarios han de esforzarse para controlar de forma efectiva como su información fluye en estos servicios. Para ofrecer a los usuarios un marco de privacidad más robusto, trabajos recientes proponen un nuevo paradigma para controles de acceso basado en relaciones. A diferencia de los controles de acceso tradicionales donde los permisos se otorgan en base a usuarios y sus roles, este paradigma emplea elementos sociales como la relación entre el propietario de la información y su audiencia potencial (por ejemplo, sólo mis hermanos pueden ver la foto). Los controles de acceso que siguen este paradigma ofrecen a los usuarios mecanismos para el control de la privacidad que representan de una forma más natural como los humanos razonan sobre cuestiones de privacidad. Además, estos controles de acceso pueden lidiar con problemáticas específicas que presentan las redes sociales. Específicamente, los usuarios comparten de forma habitual información que atañe a muchas personas, especialmente a otros miembros de la red social. En tales situaciones, dos o más personas pueden tener preferencias de privacidad que entran en conflicto. Cuando esto ocurre, no hay una configuración correcta de privacidad que sea evidente. Estas situaciones son normalmente identificadas como escenarios de privacidad multiusuario. Dado que los controles de acceso basados en relaciones son complejos para el usuario promedio de redes sociales, los proveedores de servicios no los han adoptado. Por lo tanto, para permitir la implementación de tales controles de acceso en redes sociales actuales, es necesario que se ofrezcan herramientas y mecanismos que faciliten su uso. En este sentido, esta tesis presenta cinco contribuciones: (1) una revisión del estado del arte en manejo de privacidad en redes sociales que permite identificar los retos más importantes en el campo, (2) BFF, una herramienta para obtener automáticamente la intensidad de los vínculos personales y las comunidades de usuarios, (3) un nuevo control de acceso que emplea comunidades, identificadores individuales, la intensidad de los vínculos personales, y etiquetas de contenido, (4) un modelo novedoso para representar y razonar sobre escenarios de privacidad multiusario que emplea tres tipos de características: factores contextuales, preferencias de usuario, y argumentos de usuario; y, (5) Muppet, una herramienta que recomienda configuraciones de privacidad en escenarios de privacidad multiusuario.En els darrers anys, els servicis de xarxes socials, com Facebook o LinkedIn, han experimentat un creixement exponencial. Els usuaris valoren positivament les seues variades funcionalitats com la compartició de fotos o la cerca d'amics i treball. En general, els usuaris aprecien els beneficis que les xarxes socials els aporten. No obstant això, mentre l'ús de les xarxes socials s'ha convertit en rutina per a molta gent, bretxes de privacitat que poden ocórrer en xarxes socials han augmentat els recels dels usuaris. Per exemple, és senzill trobar notícies sobre persones que han perdut el seu treball per alguna cosa que compartiren a una xarxa social. Per facilitar la definició dels ajustos de privacitat, els proveïdors de servicis empren controls d'accés senzills que normalment es basen, de forma exclusiva, en llistes o cercles d'amics. Encara que aquests controls d'accés són fàcils d'emprar per a un usuari mitjà, investigacions recents indiquen que aquests manquen elements com la força dels vincles personals, que juguen un paper clau en com els usuaris decideixen què compartir i amb qui. A més a més, malgrat la simplicitat dels controls d'accés, investigacions sobre privacitat en xarxes socials revelen que els usuaris han d'esforçar-se per a controlar de forma efectiva com fluix la seua informació en aquests servicis. Per a oferir als usuaris un marc de privacitat més robust, treballs recents proposen un nou paradigma per a controls d'accés basat en relacions. A diferència dels controls d'accés tradicionals on els permisos s'atorguen segons usuaris i els seus rols, aquest paradigma empra elements socials com la relació entre el propietari de la informació i la seua audiència potencial (per exemple, sols els meus germans poden veure aquesta foto). Els controls d'accés que segueixen aquest paradigma ofereixen als usuaris mecanismes per al control de la privacitat que representen d'una forma més natural com els humans raonen sobre la privacitat. A més a més, aquests controls d'accés poden resoldre problemàtiques específiques que presenten les xarxes socials. Específicament, els usuaris comparteixen de forma habitual informació que concerneix moltes persones, especialment a altres membres de la xarxa social. En aquestes situacions, dues o més persones poden tindre preferències de privacitat que entren en conflicte. Quan açò ocorre, no hi ha una configuració de privacitat correcta que siga evident. Aquestes situacions són normalment identificades com escenaris de privacitat multiusari. Donat que els controls d'accés basats en relacions són complexos per a l'usuari mitjà de xarxes socials, els proveïdors de servicis no els han adoptat. Per tant, per a permetre la implementació d'aquests controls d'accés en xarxes socials actuals, és necessari oferir ferramentes i mecanismes que faciliten el seu ús. En aquest sentit, aquesta tesi presenta cinc contribucions: (1) una revisió de l'estat de l'art en maneig de privacitat en xarxes socials que permet identificar els reptes més importants en el camp, (2) BFF, una ferramenta per a obtenir automàticament la força dels vincles personals i les comunitats d'usuaris, (3) un nou control d'accés que empra comunitats, identificadors individuals, força dels vincles personals, i etiquetes de contingut, (4) un model nou per a representar i raonar sobre escenaris de privacitat multiusari que empra tres tipus de característiques: factors contextuals, preferències d'usuari, i arguments d'usuaris; i, (5) Muppet, una ferramenta que recomana configuracions de privacitat en escenaris de privacitat multiusuari.López Fogués, R. (2017). Enhancing Privacy Management on Social Network Services [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/85978TESISCompendi

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

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available

Ontology-based access control for social network systems

As the information flowing around in social network systems is mainly related or can be attributed to their users, controlling access to such information by individual users becomes a crucial requirement. The intricate semantic relations among data objects, different users, and between data objects and users further add to the complexity of access control needs. In this paper, we propose an access control model based on semantic web technologies that takes into account the above mentioned complex relations. The proposed model enables expressing much more fine-grained access control policies on a social network knowledge base than the existing models. We demonstrate the applicability of our approach by implementing a proof-of-concept prototype of the proposed access control framework and evaluating its performance