Potential Friends Research Articles

Joint friend and item recommendation based on multidimensional feature reciprocal interaction in social e-commerce

Social e-commerce platforms need to undertake the two core tasks of recommending potential social friends and preferred consumption items for users. However, the use of traditional one-dimensional information is no longer able to accurately make personalized recommendations. Early scholars have confirmed that users’ social and consumption behaviors do not exist independently: users with the same interests are more likely to become friends, and there is a high probability of similar consumption behaviors among friends. In this paper, we propose a joint friend and item recommendation model based on multidimensional feature reciprocal interaction (MFRI). Which is based on the user’s social friends and item preference information, extracts the shallow and deep features of the user’s social and consumption behaviors, and utilizes the reciprocity between unusual behaviors to achieve mutual enhancement. The reciprocity between shallow and deep features in similar behaviors is also explored based on the attention mechanism, and the model is trained by a joint loss function. We conducted experiments on real datasets, and the results confirm the effectiveness and robustness of MFRI for potential friend and preference item recommendations.

“Not Sure How to Approach Them the Right Way”: Nondisabled Students’ Perspectives on Friendship With Peers With I/DD

Friendships are personally valuable and developmentally important relationships for all people, yet friendships between students with and without intellectual and/or developmental disabilities (I/DD) remain infrequent, even in inclusive settings. Extant research indicates that opportunity barriers may play a more prominent role in friendship development than the social skills of students with I/DD. Furthermore, friendships are reciprocal and mutual relationships involving two or more people. Thus, I situated this study within the social context for friendship rather than focusing only on the skills—and presumed deficits—of students with I/DD. As peers without disabilities are an integral part of that social context, I examined nondisabled students’ perspectives on friendship via four focus group interviews with 44 first to eleventh graders. Thematic findings indicated that students with I/DD were not viewed as potential friends and that students with and without I/DD had few opportunities to interact authentically.

A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach

Existing recommender system algorithms often find it difficult to interpret and, as a result, to extract meaningful recommendations from social media. Because of this, there is a growing demand for more powerful algorithms that are able to extract information from low-dimensional spaces. One such approach would be the cutting-edge matrix factorization technique. Facebook is one of the most widely used social networking platforms. It has more than one billion monthly active users who engage with each other on the platform by sharing status updates, images, events, and other types of content. Facebook's mission includes fostering stronger connections between individuals, and to that end, the platform employs techniques from recommender systems in an effort to better comprehend the actions and patterns of its users, after which it suggests forming new connections with other users. However, relatively little study has been done in this area to investigate the low-dimensional spaces included within the black box system by employing methods such as matrix factorization. Using a probabilistic matrix factorization approach, the interactions that users have with the posts of other users, such as liking, commenting, and other similar activities, were utilized in an effort to generate a list of potential friends that the user who is the focus of this work may not yet be familiar with. The proposed model performed better in terms of suggestion accuracy in comparison to the original matrix factorization, which resulted in the creation of a recommendation list that contained more correct information.

A heterogeneous graph neural recommendation model with hierarchical social trust

In traditional social recommendation algorithms, methods based on Graph Neural Networks (GNNs) representation of social networks are widely used, but the increasing sparsity of data and the tendency of users’ social influence to be complex make it difficult for the algorithms to improve the recommendation quality efficiently. To address the issues, a heterogeneous graph neural recommendation model with hierarchical social trust (GraphNON) is proposed. The algorithm is improved by fully exploiting user-friend relationships and modeling different propagation channels of social influence in multiple ways respectively. Firstly, different types of potential friends are mined based on user-user social graph and user-item rating graph respectively. Different dynamic influences of direct friends and indirectly friends among users are modeled through hierarchical influence propagation. Secondly, associations between items are mined from user-item rating graph, and influence transmission among items is modeled through hierarchical influence propagation. Finally, updated user characteristics and item characteristics are used to predict whether a user is interested in an item or not. It is shown on both datasets that the model has significant improvement in both hit rate (HR) and normalized discounted cumulative gain (NDCG) metrics compared to the optimal benchmark model. For a potential feature dimension of 64, the HR of the Yelp dataset improved by 18.3% and the NDCG by 29.3%, while the HR of the Flickr dataset improved by 31.4% and the NDCG by 37.1%.

Preexisting social ties among Auschwitz prisoners support Holocaust survival

Survivor testimonies link survival in deadly POW camps, Gulags, and Nazi concentration camps to the formation of close friendships with other prisoners. To provide evidence free of survival bias on the importance of social ties for surviving the Holocaust, we study individual histories of 30 thousand Jewish prisoners who entered the Auschwitz-Birkenau concentration camp on transports from the Theresienstadt ghetto. We ask whether the availability of potential friends among fellow prisoners on a transport influenced the chances of surviving the Holocaust. Relying on multiple proxies of preexisting social networks and varying social-linkage composition of transports, we uncover a significant survival advantage to entering Auschwitz with a larger group of potential friends.

Graph Neural Networks with Motisf-aware for Tenuous Subgraph Finding

Tenuous subgraph finding aims to detect a subgraph with few social interactions and weak relationships among nodes. Despite significant efforts made on this task, they are mostly carried out in view of graph-structured data. These methods depend on calculating the shortest path and need to enumerate all the paths between nodes, which suffer the combinatorial explosion. Moreover, they all lack the integration of neighborhood information. To this end, we propose a novel model named Graph Neural Network with Motif-aware for tenuous subgraph finding (GNNM), a neighborhood aggregation-based GNN framework that can capture the latent relationship between nodes. We design a GNN module to project nodes into a low-dimensional vector combining the higher-order correlation within nodes based on a motif-aware module. Then we design greedy algorithms in vector space to obtain a tenuous subgraph whose size is greater than a specified constraint. Particularly, considering that existing evaluation indicators cannot capture the latent friendship between nodes, we introduce a novel Potential Friend concept to measure the tenuity of a graph from a new perspective. Experimental results on the real-world and synthetic datasets demonstrate that our proposed method GNNM outperforms existing algorithms in efficiency and subgraph quality.

What Happens in Initial Interactions Forecasts Relationship Development: Showcasing the Role of Social Behavior

We do not know what happens in initial interactions to spark platonic or romantic relationships. This requires data on relationships from their inception, tracked over time. Building on theory about relationship promotion, we identified three exemplar behaviors to test novel hypotheses about relationship development. When starting college, a greenhouse for relationship initiation, first-year undergraduates (N = 143) reported initial interactions with potential friends and romantic partners, and then 129 of them reported back about those 591 people over the semester. As predicted, reports of each behavior—affectionate touch, shared laughter, and partner’s gratitude expression—were associated with immediate interest in affiliating with the new person, beyond their perceived warmth, competence, and attractiveness; theoretically derived social perceptual mechanisms explained these links. Critically, although not all potential connections blossomed into relationships, these behavioral precursors to relationship promotion predicted relationship development via post-interaction interest in affiliating. Findings are contextualized within attraction literature with implications for relationship development.

A Personalized Explainable Learner Implicit Friend Recommendation Method

With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability.

Role of PARP Inhibitors in Cancer Immunotherapy: Potential Friends to Immune Activating Molecules and Foes to Immune Checkpoints.

Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) induce cytotoxic effects as single agents in tumors characterized by defective repair of DNA double-strand breaks deriving from BRCA1/2 mutations or other abnormalities in genes associated with homologous recombination. Preclinical studies have shown that PARPi-induced DNA damage may affect the tumor immune microenvironment and immune-mediated anti-tumor response through several mechanisms. In particular, increased DNA damage has been shown to induce the activation of type I interferon pathway and up-regulation of PD-L1 expression in cancer cells, which can both enhance sensitivity to Immune Checkpoint Inhibitors (ICIs). Despite the recent approval of ICIs for a number of advanced cancer types based on their ability to reinvigorate T-cell-mediated antitumor immune responses, a consistent percentage of treated patients fail to respond, strongly encouraging the identification of combination therapies to overcome resistance. In the present review, we analyzed both established and unexplored mechanisms that may be elicited by PARPi, supporting immune reactivation and their potential synergism with currently used ICIs. This analysis may indicate novel and possibly patient-specific immune features that might represent new pharmacological targets of PARPi, potentially leading to the identification of predictive biomarkers of response to their combination with ICIs.

From Strangers to Friends: Tie Formations and Online Activities in an Evolving Social Network

The authors study how strangers become friends within an evolving online social network. By modeling the coevolution of individual users’ friendship tie formations (when and with whom) and their concurrent online activities, the authors uncover important drivers underlying individuals’ friendship decisions and, at the same time, quantify the resulting peer effects on individuals’ actions. They estimate their model using a novel data set capturing the continuous development of a network and users’ entire action histories within the network. The results reveal that similarity (homophily) with a potential friend, the properties of a potential friend's network, and the potential friend’s domain expertise all play a role in friendship formation. Via prediction exercises, the authors find that stimulating anime watching is the most effective sitewide intervention, which leads to the highest overall site traffic and the largest number of active users, and that recommending a friend of a friend as a potential friend is the most effective strategy in stimulating friendship tie formation. In contrast to the common finding for static networks, the results indicate that seeding to users with the most friends is not the most effective strategy to increase users’ activity levels in an evolving network.

Self-Configured Framework for scalable link prediction in twitter: Towards autonomous spark framework

Scalable link prediction in social networks allow dynamic social interaction gathering, potential friend suggestions, and community detection. Distributed open-source frameworks such as Hadoop and Spark facilitate efficient link prediction especially in large-scale social networks. The frameworks provide different kinds of tunable properties for users to manually configure the parameters for the applications. However, manual configurations are open to performance issues when the applications start scaling tremendously, which are hard to set up and are exposed to human errors. This paper proposes a novel Self-Configured Framework (SCF) to provide an autonomous feature in Spark that predicts and sets the best configuration instantly before the application execution using the XGBoost classifier. The framework with a self-configuration setting demonstrates a 40% reduction in prediction time as well as a balanced resource consumption that makes full use of resources, especially for limited number and size of clusters. The presented framework establishes its efficiency for link prediction in large-scale social networks by automatically configuring the best configuration suitable for a specific application given the varying dataset size of the Twitter social network, workload, and cluster specification.

Facing Familiar Strangers and Potential Friends: Rumours of Betrayal, Ambiguous Friendships and the Dangers of Poison in Urban Madagascar

In this article, I explore the different ways in which educated young people in urban Mahajanga experience and anticipate social relationships. Taking rumours of betrayal as my starting point, I shed light on the ambivalent nature of friendship and commensality. While both phenomena echo Malagasy values encompassing solidarity and sharing, they also contain the threat of switching into dangerous territory: when trust becomes mistrust, a close friend can well become an enemy and a commensal situation might ultimately be poisoned. This article focuses on a relatively affluent group of young people and examines how narratives of betrayal both reflect and contribute to general frustrations and worries about personal failure. Individual life courses revolve essentially around a social project of anticipation which includes not only hope and aspiration but also fear of potential failure and of arousing envy among others. I argue that these generalised anxieties do not stifle social interaction; instead they foster an attitude of suspicion that serves as a useful tool of temporary detachment, giving space to gather knowledge about someone or something before determining the appropriate response.

Potential friendship discovery in social networks based on hybrid ensemble multiple collaborative filtering models in a 5G network environment

At present, 5G network technology is being applied to various social network modes, and it can provide technical and traffic support for social networks. Potential friendship discovery technology in 5G-enabled social networks is beneficial for users to make potential friends and expand their range of activities and social hierarchy, which is highly sought after in today's social networks and has great economic and application value. However, the sparsity of the dominant user association dataset in 5G-enabled social networks and the limitations of traditional collaborative filtering algorithms are two major challenges for the friend recommendation problem. Therefore, in order to overcome these problems regarding previous models, we propose a Hybrid Ensemble Multiple Collaborative Filtering Model (HEMCF) for discovering potential buddy relationships. The HEMCF model draws on a special autoencoder method that can effectively exploit the association matrix between friends and additional information to extract a hidden representation of users containing global structural information. Then, it uses the random walk-based graph embedding algorithm DeepWalk to extract another hidden representation of users in the buddy network containing local structural information. Finally, in the output module, the HEMCF model stacks and multiplies the two types of hidden representations of users to ensure that the information mentioned above is concentrated in the final output to generate the final prediction value. The magnitude of the prediction value represents the probability of the users being friends, with larger values representing a high probability of the two users being friends, and vice versa. Experimental results show that the proposed method boosts the accuracy of the relationship prediction over baselines on 3 real-world public datasets dramatically.

Revisiting Local Walking Based on Social Network Trust (LWSNT): Friends Recommendation Algorithm in Facebook Social Networks

In the last decades, the internet penetration rate and online social network users have grown very fast. Online social network, such as Facebook, is a platform where one can find friends without having to meet face to face. A social network is represented by a large graph because it involves many participants. Hence, it is hard to find potential friends who have the same thoughts and interests. The Local Walking Based on Social Network Trust (LWSNT) algorithm is one of the popular algorithms for social friend recommendation. This study re-examines whether the correlation between attributes gives un-match ranks in different cases (cases with and without correlation). We assess the performance of LWSNT in Facebook networks under the supervised manner by comparing its F-score against similar methods. By using Kendall’s tau correlation, the results show that the correlation of attributes has no significant effect on the order of friend recommendations. In addition, the LWSNT performance is quite inferior against the Common Neighbors algorithm and Jaccard index.

On sharing and caring: Investigating the effects of a robot's self-disclosure and question- asking on children's robot perceptions and child-robot relationship formation

Although scholars have focused on the role of self-disclosure in the context of child-robot interaction and relationship formation, little is known as to how the effects of a robot's self-disclosure vary by the information the robot shares. Moreover, the influence of a robot's question-asking on children's perception of, and relationship formation with the robot remains understudied. We therefore investigated experimentally, with a 2 × 2 between-subjects design, how children perceive and relate to a social robot when the robot engages in (personal) self-disclosure versus (factual) self-description and when the robot does or does not ask them questions. We collected self-report data from 293 children aged 7 to 10, who interacted with the Nao robot in a science museum. The robot's question-asking increased children's trust in the robot as well as their belief in its cognitive perspective-taking abilities. Self-disclosure, in contrast, decreased children's perception of the robot's capacity to adopt their affective perspective. Children's consideration of the robot as a social actor and a potential friend did not differ across conditions. A post-hoc analysis showed that the effect of question-asking on trust was mediated by children's perception of the robot's cognitive perspective-taking abilities.

Information fusion oriented heterogeneous social network for friend recommendation via community detection

The rapid advance of online social networks and the tremendous growth in the number of participants and attention have led to information overload and increased the difficulty of making accurate recommendations of new friends. Existing recommendation methods based on semantic similarity, social graphs, or collaborative filtering are unsuitable for very large social networks because of their high computational cost or low effectiveness. We present an approach entitled Hybrid Recommendation Through Community Detection (HRTCD) for friend prediction with linear runtime complexity that makes full use of the characteristics of social media based on hybrid information fusion. It extracts the content topics of microblog for each participant along with the appraisal of domain-dependent user impact, builds a small-size heterogeneous network for each target user by fusing the interest similarity and social interaction between individuals, discovers all of the implicit clusters of target user via a community detection algorithm, and establishes the recommendation set consisting of a fixed number of potential friends. Experimental results on both the synthetic and real-world social networks demonstrate that our scheme provides a higher prediction rating and significantly improves the recommendation accuracy and offers much faster performance.

A learning to rank framework for future friendship ranking in streaming heterogeneous social networks

SummaryFriendship prediction is an important aspect of a social network. Social network users rely a lot on friendship suggestion as it helps them in improving their network of friends. Moreover it has a huge impact in analyzing whom one is influenced by. In order to know this information it is not only essential to identify the potential friends, but also rank them to quantify their influence. To do this, we propose a learning to rank framework using the most popular machine learning technique, LambdaMART with a new boosting algorithm. Our framework provides unparalleled values of normalized discounted cumulative gain measure. We also analyze which feature of the social network is helpful in getting these nonpareil values. Our boosting algorithm yields an analytical solution for the over‐fitting problem with the increasing number of iterations, thereby augmenting the robustness of the framework.

Artificial Intelligence for Image Interpretation: Point—The Radiologist's Potential Friend

This article does not include an abstract. Please see the accompanying Counterpoint by Frank J. Lexa and Saurabh Jha.

A Potential Friend Recommendation Algorithm for Obtaining Spatial Information

With the rapid development of social network, friend recommendation algorithm has become an important component of social application. Location-based social network (LBSN) enables users to record and share their locations anytime and anywhere, which is a high quality information source. In order to meet people's demand of expanding social circle and obtaining diversified spatial information when making friends, this paper proposes a potential friend recommendation algorithm based on the similarity of user's check-in behavior and spatial information acquisition level in the real world. Firstly, we employ kernel density estimation and time entropy to solve the problems of data sparsity and low concentration, then employ cosine distance to measure the check-in behavior similarity. Secondly, we analyze users’ spatial distribution of check-in location and cognitive differences on spatial information. Finally, the method mentioned above is tested with dataset called Foursquare. The results of the experiment show that the proposed method has competitive performance.

Chavirer par Lola Lafon

Reviewed by: Chavirer par Lola Lafon William Cloonan Lafon, Lola. Chavirer. Actes Sud, 2020. ISBN 978-2-330-13934-6. Pp. 345. The verb, chavirer, aside from its literal meanings, "to capsize," "to overturn," contains the figurative sense of "to vacillate." For Cléo, the main character in Chavirer, and for women like her coming of age in the 1980s, this is the connotation which matters. Cléo, the product of a petit-bourgeois milieu, wants to be a modern jazz dancer. She is talented, but not a potential star. One day a woman named Cathy approaches the thirteen-year-old on behalf of the Fondation Galatée whose mission is to encourage young talent from underserved communities. The Fondation has several levels of vetting, which Cléo passes through easily. At the final stage, she is invited to an elegant lunch hosted by a number of male judges who praise her achievements and maturity. The moment seems triumphal until one of the judges begins to molest her: "[L]a langue de Jean-Christophe était comme une huître dans sa bouche, morte et vivante, mouillée et visqueuse" (54). Cléo is stunned, and at one point wonders whether the fault was hers since her aggressor called her frigid. In her confusion she briefly works as Cathy's recruiter of prospective boursières for the foundation. The years pass, and Cléo's sporadic efforts to come to grips with her traumatic experience really advance no further than "à default du pardon, laisse venir l'oubli" (276). Eventually she becomes a dancer in places like the Lido where she experiences a different sort of exploitation, one that is more financial than overtly sexual, yet where her body, along with the sequins, feathers and garish make-up, is just one more prop. More attracted to women than to men, Cléo nonetheless marries and has a daughter. Her life seems to have settled into middle-class respectability, except for the occasional frisson her daughter's friends experience when they learn she was once a showgirl. Yet the world was changing and by the 2010s #METOO was having an impact on French women. Two female documentary makers decide to do a film on the not-so-mysterious Fondation Galatée. Their initial efforts to interview individual women, now middle-aged or older, who had been solicited by the Fondation prove fruitless. However, when they announce on line their documentary project and invite former candidates to meet with them in a large hall (a casting call of a very different order), many women respond. Here, Cléo encounters Betty, a woman who was her school mate but also rival for a bourse at that time. Now she is an overweight, charming eccentric whose current passion is saving and protecting animals. Yet the former sœurs ennemies have changed as society has changed, and now they see themselves as mutual victims and potential friends. Neither will ever forgive Galatée, yet their earlier vacillation has disappeared. They will never forget what they endured, and will make it harder for other women to suffer the same fate. [End Page 250] William Cloonan Florida State University, emeritus Copyright © 2021 American Association of Teachers of French