Location Sensitive Friend Recommendation in Social Network

Friend recommendation with content spread enhancement in social networks

Virtual friend recommendations in virtual worlds

Friend recommendation is a fundamental service for both social networks and practical applications. The majority of existing friend-recommendation methods utilize user profiles, social relationships, or static post content data, but rarely consider the semantic intentions and dynamic behaviors of users. In this paper, we propose FRec++, a friend recommendation method based on semantic and dynamic information. In FRec++, the first plus stands for new friend recommendation using a semantic model based on LDA while the next plus stands for the use of dynamic user attributes (e.g., behaviors or positions). More specifically, we first use the LDA method to generate semantic topics for user interests, and then compute the topic similarities between the target user and candidate friends. Next, we calculate the similarities of dynamic behaviors (i.e., forwarding, making comments, liking, and replying) and investigate the static attributes of users to measure the relevance of preferences. Finally, the weighted average method is used to integrate the above factors. We conducted experiments on the Weibo dataset and the results show that FRec++ outperforms several existing methods.

Location information is very important in the real world. How to detect users' locations and further movement trajectories automatically is significant for many location-based services such as dietary recommendation and tourism planning. With the rapid development of social media such as Sina Weibo and Twitter, more and more people publish posts at any time which contain their real-time location information. This makes it possible to detect users' movement trajectories automatically by social media. In this paper, we propose a novel method to detect city-level locations and further identify movement trajectory for a given user in social media. Our efforts are made in two phases: (1) location detection, and (2) movement trajectory identification. Phase (1) combines two components, i.e. a Bayes model based on words distribution over locations and the social relationships of users in social media, to detect or infer users' locations. The former one is utilized to judge whether the content of a post contains explicit/implicit location information and further to detect the location. The latter one assumes that friends tend to gather together when they talk about the same thing. Thus, it infers a user's locations from his friends' detected locations in social media. Phase (2) constructs the user's movement trajectory by smoothing these detected locations in first phase according to both semantic context of posts and transfer time between locations. Experiments on real dataset from Sina Weibo demonstrate that our approach can outperform baseline methods significantly in terms of Precision, Recall, F1, Error Distance (ErrDist), Detect Ratio (DeteRatio), T-Precision, T-Recall and T-F1, respectively.

Recently, communication and sharing the information through online social network (OSN) grows very fast in day to day life. As user share important data through online social network, therefore providing security and privacy to the online social network becomes the challenging task for the researchers. Moreover, for increasing the social connections and get the information from the particular group of people user needs to search the new friends and add them in the friend list. Now a days there isa friend recommendation method which was used by many online social networks, this method recommended the friend list to the user. As the privacy of these friend recommendation methods is the most significance task for the researchers and for the online social networks. Basically an online social network focused on the millions people's profile to spend time on an online social network, also implementing an OSN clients social circles, by using recommended suggestions. In this system, the main focus is on privacy and security of the online social network. This system basically focuses to support the users of online social network by secure trust creation with a stranger. This is achieved with multi-hop recommendation process. In the proposed method, the system provides privacy to the online social network from which system is allowing users to enhance their social networks. Existing system used secure KNN algorithm for securing social network. By using KNN algorithm, distance based learning is unclear. It is sort of distance and their component to use for the best results and calculation is very expensive. To overcome this limitation and to improve the precision, the proposed frame-work makes use of secure social coordinate matching with a Naive Bayes classifier. Also the privcy of social coordinates is maintained with Pailier encryption algorithm. Experimental results demonstrate that the proposed system is secure and time efficient than the existing one. Memory consumption is also improved with the proposed system.

Navigating our complex social lives requires understanding where others sit in our social networks. Some individuals may be more attuned to the structure of their social world, and thus, better able to learn new social networks due to having accumulated accurate priors about social network structure in their own lives. Correspondingly, such individuals may acquire more advantageous positions in their own social networks. In four studies (Ntotal = 1,768), brokers (people who connect otherwise disparate people in their own networks) were especially good at learning and remembering new networks that were structured like typical real-world social networks, but not atypically structured ones, suggesting that brokers are attuned to the structure of real-world networks. Additionally, we found that brokers were able to learn networks better by focusing on ties that exist in those networks (as opposed to focusing on ties that were missing) and other brokers. We found no differences when the network was framed as a social network of friends or a nonsocial network of flights between airports. This work illuminates the mechanisms of network learning based on one's own experiences and establishes links between one's own social network position and one's ability to learn new networks. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

In this chapter we examine the social nature of lone mothers’ lives as lived within particular neighbourhoods, as they interact with kin and friends in social networks. In other words, we look at what some theorists call ‘social capital’; that is, those features of social organisation, such as norms, values, expectations and networks of social support, which facilitate co-operation and trust between people for their mutual benefit. In turn, these features are seen as decisive in enhancing — or undermining — the economic health of localities and of those living within them. Indeed, without strong local traditions of active reciprocal engagement, embodied in social networks and dominant norms, state economic and social initiatives are thought likely to fail. Stocks of social capital, as collective action, thus tend to be self-reinforcing and cumulative — the more social capital an area has, the more it will generate and the more prosperous it will become. Conversely, the less social capital an area has, the less it will generate, and the poorer it will become. According to this view the individualism and pursuit of self-interest so beloved of free market theorists is in reality an economic dead end; rather, it is collective ties of social and civic trust and mutuality which are the key ingredients of economic prosperity (Putnam 1993a, b; Fukuyama 1995; Wann 1995; see Chapter 8 for further discussion).

A novel context-aware recommendation algorithm with two-level SVD in social networks

Social networks have become very popular in today’s age. As a result, Friend Recommendation Systems (FRSs) have gained immense importance. Teenagers today are often depressed and feel lonely. Social Networks, if used wisely can help a user connect to the right set of friends which can help to solve the above-mentioned challenges. Most popular social media platforms rely on homophily-based FRSs for their friend recommendations. That is, they use the user’s social graph to match him/her with other users. Users now are concerned about how their personal data is being used and its privacy. Therefore, most users do not reveal their personal information on social networks. Also, there is no evidence to the accuracy of user information. Therefore, the existing recommendation systems in social media prove to be inaccurate in a lot of cases. Furthermore, the main purpose of these traditional recommendation systems is to connect pre-existing friends online rather than suggesting users that could be potential friends. To connect users to a new set of people, there is a need to design a friend recommendation system that matches users based on something other than existing connections. In this paper, we propose a system which uses personality analysis along with hybrid filtering to make personalized friend recommendations along with other filtering options for age range, location, gender, etc. This system would solve the cold start problem which occurs because in the present scenario people don’t like spending time to specify their preferences for friend recommendations. The filtering options are of further interest to the user as there is a possibility of connecting to a new bunch of friends in person too.

An important function of online social networks (OSNs) is to help users find or add friends with common interests. In order to improve the quality of friends-searching results, a model for ranking these results is proposed in this paper with the introduction of two novel concepts named trust and popularity along with their calculation algorithm. The improved shortest path algorithm with the introduction of a threshold is used for computing the trust value in efficiency, and the page rank algorithm is used for solving the popularity value iteratively. Then the combination algorithm for these two values is performed to generate the final searching output. Experimental results show that this model can provide more satisfactory searching results for users, and provides wonderful supports for friend searching and friend recommendation in OSNs. This model has been deployed in “Scene Life”, a mobile social networking system of Nokia Research Center Beijing.

The objective of this study was to compare the digital competences of university students before and during the COVID-19 pandemic, and to determine the impact of internet use on these competences, specifically those related to social communication, collaborative learning, as well as information searching and processing. The sample comprised 415 Spanish students who began university studies between 2019 and 2022. The instrument used was the COBADI. The results indicate an increase in internet use to talk with friends in social networks and work with classmates, such as in collaborative homework. In terms of digital competences during the pandemic, there was an increase in competences related to social communication, collaborative learning, and information searching and processing. The study also found that academic and recreational internet use predicted basic digital competences, as determined by a MANCOVA analysis. The role of the pandemic context in increasing basic digital competences in university students is discussed.

Aiming to represent user characteristics and personal interests, the task of user profiling is playing an increasingly important role for many real-world applications, e.g., e-commerce and social networks platforms. By exploiting the data like texts and user behaviors, most existing solutions address user profiling as a classification task, where each user is formulated as an individual data instance. Nevertheless, a user's profile is not only reflected from her/his affiliated data, but also can be inferred from other users, e.g., the users that have similar co-purchase behaviors in e-commerce, the friends in social networks, etc. In this paper, we approach user profiling in a semi-supervised manner, developing a generic solution based on heterogeneous graph learning. On the graph, nodes represent the entities of interest (e.g., users, items, attributes of items, etc.), and edges represent the interactions between entities. Our heterogeneous graph attention networks (HGAT) method learns the representation for each entity by accounting for the graph structure, and exploits the attention mechanism to discriminate the importance of each neighbor entity. Through such a learning scheme, HGAT can leverage both unsupervised information and limited labels of users to build the predictor. Extensive experiments on a real-world e-commerce dataset verify the effectiveness and rationality of our HGAT for user profiling.

Exercise is demonstrated to be beneficial to both physical and mental wellbeing. The challenge of how to persuade individuals to maintain regular exercise has received attention from multiple disciplines. With the rise of online social networks, a number of studies found positive correlations between online social support and active lifestyle. In this paper, we report findings from a 52 weeks study under the methods of phynomenography to analyze the roles played by friends in social networks and the support in exercise behavior changes related to these relationships. The results show that besides the individual effect from message tailoring and intervention strategies, the interaction ecosystem based on appropriate role playing by leaders, peers and followers can enhance persuasion power. Effective social support for encouraging physical activities should consider the roles played by the target individuals in the environment and provide comfortable role support.

The aim of this paper is to explore how silence, secrecy and censorship related to sexual harassment are fragmented through dialogue and information sharing among social networks, leading to different types of social support given and received. In-depth interviews with 40 respondents and focus group discussions with four groups were carried out under the qualitative research approach. The theory of social support was employed as the theoretical basis of the study. The findings indicated that through dialogue and information sharing related to sexual harassment within social networks of friends, family, co-workers and sometimes superiors, women gain and give emotional, informational, instrumental and appraisal support, emphasizing the many instances when the issue becomes open, non-privatized, and social. Stemming from these findings, implications for managers in terms of using social support as a means of dealing with sexual harassment in organizations are discussed.

With the popularity of social media platform and feminism on the internet, the marketing activities on female issues also known as femvertising has become a new and promising field of brand marketing. Considering the growing female awareness in social networks and the lack of quantitative studies, online brand marketing on female and gender issues remains a tough challenge for enterprises particularly. In this paper, we investigate three real cases of brand marketing on female issues and quantitate assessment of different-gender customers' attitudes towards femvertising through social media networks is conducted. Representative machine learning methods like SVM and state of art deep learning methods: Bert with LSTM and Bert with Attention mechanism plus LSTM model are compared to train the sentiment classification model for Weibo posts. Among them, Bert with Attention mechanism plus LSTM model outperforms others with an f1 macro score of 98.0998%. Then we use the best model to predict the polarity of posts. It turns out that female customers are more sensitive to femvertising than male ones. And femvertising focusing on empowering girls and women could be a great boost to positive influence on brand building. While simply employing controversial celebrities on gender issues won't please either side.