Friends, Strangers, and the Value of Ego Networks for Recommendation

Two main approaches to using social network information in recommendation have emerged: augmenting collaborative filtering with social data and algorithms that use only ego-centric data. We compare the two approaches using movie and music data from Facebook, and hashtag data from Twitter. We find that recommendation algorithms based only on friends perform no worse than those based on the full network, even though they require much less data and computational resources. Further, our evidence suggests that locality of preference, or the non-random distribution of item preferences in a social network, is a driving force behind the value of incorporating social network information into recommender algorithms. When locality is high, as in Twitter data, simple k-nn recommenders do better based only on friends than they do if they draw from the entire network. These results help us understand when, and why, social network information is likely to support recommendation systems, and show that systems that see ego-centric slices of a complete network (such as websites that use Facebook logins) or have computational limitations (such as mobile devices) may profitably use ego-centric recommendation algorithms.Comment: 5 pages, ICWSM 201

Comparative social capital: Networks of entrepreneurs and investors in China and Russia

Most studies on entrepreneurs’ networks incorporate social capital and networks as independent variables that affect entrepreneurs’ actions and its outcomes. By contrast, this article examines social capital of the Chinese and Russian entrepreneurs and venture capitalists as dependent variables, and it examines entrepreneurs’ social capital from the perspectives of institutional theory and cultural theory. The empirical data are composed of structured telephone interviews with 159 software entrepreneurs, and the data of 124 venture capital decisions in Beijing and Moscow. The study found that social networks of the Chinese entrepreneurs are smaller in size, denser in structure, and more homogeneous in composition compared to networks of the Russian entrepreneurs due to the institutional and cultural differences between the two countries. Furthermore, the study revealed that dyadic (two-person) ties are stronger and interpersonal trust is greater in China than in Russia. The research and practical implications are discussed.http://deepblue.lib.umich.edu/bitstream/2027.42/40169/3/wp783.pd

Location Prediction: Communities Speak Louder than Friends

Humans are social animals, they interact with different communities of friends to conduct different activities. The literature shows that human mobility is constrained by their social relations. In this paper, we investigate the social impact of a person's communities on his mobility, instead of all friends from his online social networks. This study can be particularly useful, as certain social behaviors are influenced by specific communities but not all friends. To achieve our goal, we first develop a measure to characterize a person's social diversity, which we term `community entropy'. Through analysis of two real-life datasets, we demonstrate that a person's mobility is influenced only by a small fraction of his communities and the influence depends on the social contexts of the communities. We then exploit machine learning techniques to predict users' future movement based on their communities' information. Extensive experiments demonstrate the prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201

Studying and Modeling the Connection between People's Preferences and Content Sharing

People regularly share items using online social media. However, people's decisions around sharing---who shares what to whom and why---are not well understood. We present a user study involving 87 pairs of Facebook users to understand how people make their sharing decisions. We find that even when sharing to a specific individual, people's own preference for an item (individuation) dominates over the recipient's preferences (altruism). People's open-ended responses about how they share, however, indicate that they do try to personalize shares based on the recipient. To explain these contrasting results, we propose a novel process model of sharing that takes into account people's preferences and the salience of an item. We also present encouraging results for a sharing prediction model that incorporates both the senders' and the recipients' preferences. These results suggest improvements to both algorithms that support sharing in social media and to information diffusion models.Comment: CSCW 201

Comparative social capital: Networks of entrepreneurs and investors in China and Russia

Most studies on entrepreneurs’ networks incorporate social capital and networks as independent variables that affect entrepreneurs’ actions and its outcomes. By contrast, this article examines social capital of the Chinese and Russian entrepreneurs and venture capitalists as dependent variables, and it examines entrepreneurs’ social capital from the perspectives of institutional theory and cultural theory. The empirical data are composed of structured telephone interviews with 159 software entrepreneurs, and the data of 124 venture capital decisions in Beijing and Moscow. The study found that social networks of the Chinese entrepreneurs are smaller in size, denser in structure, and more homogeneous in composition compared to networks of the Russian entrepreneurs due to the institutional and cultural differences between the two countries. Furthermore, the study revealed that dyadic (two-person) ties are stronger and interpersonal trust is greater in China than in Russia. The research and practical implications are discussed.Social capital, entrepreneurs, venture capitalists, China and Russia.

Using Text Similarity to Detect Social Interactions not Captured by Formal Reply Mechanisms

In modeling social interaction online, it is important to understand when people are reacting to each other. Many systems have explicit indicators of replies, such as threading in discussion forums or replies and retweets in Twitter. However, it is likely these explicit indicators capture only part of people's reactions to each other, thus, computational social science approaches that use them to infer relationships or influence are likely to miss the mark. This paper explores the problem of detecting non-explicit responses, presenting a new approach that uses tf-idf similarity between a user's own tweets and recent tweets by people they follow. Based on a month's worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion.Comment: A final version of this work was published in the 2015 IEEE 11th International Conference on e-Science (e-Science

Enhancing digital business ecosystem trust and reputation with centrality measures

Digital Business Ecosystem (DBE) is a decentralised environment where very small enterprises (VSEs) and small to medium sized enterprises (SMEs) interoperate by establishing collaborations with each other. Collaborations play a major role in the development of DBEs where it is often difficult to select partners, as they are most likely strangers. Even though trust forms the basis for collaboration decisions, trust and reputation information may not be available for each participant. Recommendations from other participants are therefore necessary to help with the selection process. Given the nature of DBEs, social network centrality measures that can influence power and control in the network need to be considered for DBE trust and reputation. A number of social network centralities, which influence reputation in social graphs have been studied in the past. This paper investigates an unexploited centrality measure, betweenness centrality, as a metric to be considered for trust and reputation

Enhancing digital business ecosystem trust and reputation with centrality measures

Digital Business Ecosystem (DBE) is a decentralised environment where very small enterprises (VSEs) and small to medium sized enterprises (SMEs) interoperate by establishing collaborations with each other. Collaborations play a major role in the development of DBEs where it is often difficult to select partners, as they are most likely strangers. Even though trust forms the basis for collaboration decisions, trust and reputation information may not be available for each participant. Recommendations from other participants are therefore necessary to help with the selection process. Given the nature of DBEs, social network centrality measures that can influence power and control in the network need to be considered for DBE trust and reputation. A number of social network centralities, which influence reputation in social graphs have been studied in the past. This paper investigates an unexploited centrality measure, betweenness centrality, as a metric to be considered for trust and reputation