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

Needs Assessment of Winooski Food Shelf Clients

Food shelves offer a unique, centralized location to engage with populations with multiple needs. Food shelf clients demonstrate poorer overall health and less access to healthcare and other social services. Winooski has a large refugee and immigrant population, whose needs may differ greatly from clients served by nearby food shelves in Chittenden County. Winooski Food Shelf visits provide opportunities to link clients to other needed health and social services. Previous studies have demonstrated efficacy in resource intervention guided by information gathered from food shelf clients. PROJECT GOAL: Assess the greatest health and social needs for clientele of the Winooski Food Shelf and make recommendations based on data gathered.https://scholarworks.uvm.edu/comphp_gallery/1283/thumbnail.jp

Privacy-preserving proximity detection with secure multi-party computational geometry

Over the last years, Location-Based Services (LBSs) have become popular due to the global use of smartphones and improvement in Global Positioning System (GPS) and other positioning methods. Location-based services employ users' location to offer relevant information to users or provide them with useful recommendations. Meanwhile, with the development of social applications, location-based social networking services (LBSNS) have attracted millions of users because the geographic position of users can be used to enhance the services provided by those social applications. Proximity detection, as one type of location-based function, makes LBSNS more flexible and notifies mobile users when they are in proximity. Despite all the desirable features that such applications provide, disclosing the exact location of individuals to a centralized server and/or their social friends might put users at risk of falling their information in wrong hands, since locations may disclose sensitive information about people including political and religious affiliations, lifestyle, health status, etc. Consequently, users might be unwilling to participate in such applications. To this end, private proximity detection schemes enable two parties to check whether they are in close proximity while keeping their exact locations secret. In particular, running a private proximity detection protocol between two parties only results in a boolean value to the querier. Besides, it guarantees that no other information can be leaked to the participants regarding the other party's location. However, most proposed private proximity detection protocols enable users to choose only a simple geometric range on the map, such as a circle or a rectangle, in order to test for proximity. In this thesis, we take inspiration from the field of Computational Geometry and develop two privacy-preserving proximity detection protocols that allow a mobile user to specify an arbitrary complex polygon on the map and check whether his/her friends are located therein. We also analyzed the efficiency of our solutions in terms of computational and communication costs. Our evaluation shows that compared to the similar earlier work, the proposed solution increases the computational efficiency by up to 50%, and reduces the communication overhead by up to 90%. Therefore, we have achieved a significant reduction of computational and communication complexity

Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned

Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results con rm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions

Bias characterization, assessment, and mitigation in location-based recommender systems

Location-Based Social Networks stimulated the rise of services such as Location-based Recommender Systems. These systems suggest to users points of interest (or venues) to visit when they arrive in a specific city or region. These recommendations impact various stakeholders in society, like the users who receive the recommendations and venue owners. Hence, if a recommender generates biased or polarized results, this affects in tangible ways both the experience of the users and the providers’ activities. In this paper, we focus on four forms of polarization, namely venue popularity, category popularity, venue exposure, and geographical distance. We characterize them on different families of recommendation algorithms when using a realistic (temporal-aware) offline evaluation methodology while assessing their existence. Besides, we propose two automatic approaches to mitigate those biases. Experimental results on real-world data show that these approaches are able to jointly improve the recommendation effectiveness, while alleviating these multiple polarizationsOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been funded by the Ministerio de Ciencia e Innovación (reference PID2019-108965GB-I00) and by the European Social Fund (ESF), within the 2017 call for predoctoral contract

Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information

Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this paper, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, to model the multi dimensional contextual information. Tensor Factorization naturally extends Matrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users’ check-ins instead of the traditional two dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data which is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach

A study of neighbour selection strategies for POI recommendation in LBSNs

Location-based Recommender Systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of liked-minded people, so called neighbors, for prediction. Thus, an adequate selection of such neighbors becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbors in the context of a collaborative filtering based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighborhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from Location-based Social Networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbors based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area as well as to recommender systems developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.Fil: Rios, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

The examination of gathering points' capacity regarding mobility and accessibility: Case of Bayrakli district

The concept of accessibility that bases on continuing the flow of people, goods and services uninterruptedly is discussed as "maximum contact by minimum facility". This concept which is related with the distance has been evaluated various criteria such as physical, economic, perceptual, temporal, etc. Several assignations (optimum location, minimum distance, best distribution method, etc.) have revealed the importance of gathering points in terms of proceeding the disaster management properly. The origin point of linking "accessibility" and "transportation network" is the mobility which reveals as accessing people to adequate gathering points and services in the shortest time. These gathering points which are determined due to the specific criteria and also referred to the social infrastructure areas have a vital importance when any disaster or emergencies occur; so the site selection, availability and accessibility of these areas become extremely significant. The aim of this study is to examine the accessibility of gathering points in Bayrakli district located in Izmir city by taking minimum standards and also some recommendations into consideration. The spatial analyses based on current and potential gathering points are carried out via ArcMap software. The current and potential gathering points in Bayrakli district are examined in terms of accessibility their capacity for each neighbourhood due to their spatial distribution. According to the results, each gathering points in neighbourhoods cannot be accessible in the shortest duration by walking and also the current urban pattern affects the capacity parameter regarding the accessibility and mobility significantly. © Authors 2018. CC BY 4.0 License

The Mega-City-Region of Munich: A Kingdom of Its Own Or a Space of Inter-Connected Flows?

In a world of continuing globalization mega-city regions (MCR) are of increasing importance. MCR are defined by a high concentration of regulation, innovation and gateway functions. Planners and politicians alike recognise that competition on this new spatial scale is becoming a strategic tool to develop the whole country. So they are looking for spatial strategies to ensure economic success and sustainable development (social and spatial cohesion) at the same time. However, only little is known about the factors, which constitute MCRs, the inter-relationships within and between MCR, the primacy of individual functional urban areas within MCR and the potential methods to communicate these complex structures to politicians and stakeholders. The paper will deal with the following questions that will be tested with a case-study in the Munich metropolitan region, the so-called Greater Munich Area: (1) Which flows and inter-relationsships of knowledge based economic activities define the MCR? How is the MCR itself embedded in the global network of mega-city regions? (2) What spatial mismatch becomes recognisable when differentiating between functional and morphological polycentricity? (3) What functional polycentricity do firms in the Advanced Producer Services and in the High-Tech sectors produce when looking at their office location strategies? (4)What policy recommendations follow for sustainable spatial development? Our hypothesis is that the current perception of MCRs by local and regional stakeholders is quite inhomogeneous. There is an obvious gap between the functional logic of the competitive world and the territorial logic of the planning administrations. So we propose to look at the individual logics and the spatial reach of the stakeholders’ activites. The study will use an empirical survey for the Greater Munich Area that is mainly based on commuter data, location data of firms, business communication data and interviews with firms and a selection of key stakeholders.