Social e-commerce sites can acquire millions of users within a short time and are growing fast at an accelerated rate. In this paper, we demonstrate how these social commerce platforms develop as a blend of social relationships and economic transactions. We present the first measurement study on the full-scale data of Beidian, one of the fastest-growing social e-commerce sites in China, which involves 11.8 million users. We first analyze the topological structure of the Beidian platform and highlight its decentralized nature. We then study the site's rapid growth and its growth mechanism via invitation cascade. Finally, we investigate the purchasing network on Beidian, where we focus on user proximity and loyalty, which contributes to the site's high conversion rate. As the consequences of interactions between strong ties and economic logic, emerging social commerce demonstrates significant property deviations from all known social networks and e-commerce in terms of network structure, dynamics, and user behavior. To the best of our knowledge, this work is the first quantitative study on the network characteristics and dynamics of emerging social commerce platforms.
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Social e-commerce platforms embed shopping experiences within IM apps, e.g., WeChat, where real-world friends post and recommend products from the platform in IM group chats and quite often form lasting recommending/buying relationships. How and why do users engage in IM based social commerce? Do such platforms create novel experiences that are distinct from prior commerce? And do these platforms bring changes to user social lives and relationships? To shed light on these questions, we launch a qualitative study where we carry out 12 semi-structured interviews. We show that IM based social commerce: 1) enables more reachable, cost-reducing, and ubiquitous user shopping experiences, 2) shapes user decision-making process in shopping through pre-existing social relationship, mutual trust, shared identity, and community norm, and 3) creates novel social interactions, which can contribute to new tie formation while maintaining existing social relationships. We demonstrate that all these unique aspects link closely to the characteristics of IM platforms, as well as the coupling of user social and economic lives under such a business model. Our study provides important research and design implications for social commerce and decentralized, trusted socio-technical systems in general.
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Social e-commerce platforms leverage the stimulated word-of-mouth effect to promote the sales of items. In this paper, we investigate how people's purchase behaviors are shaped in this scenario on a full-scale purchase behavior dataset from one of the leading social e-commerce platforms, Beidian. Specifically, we conduct a comparison study between social e-commerce and conventional e-commerce to examine how social factors affect user's purchase behaviors. We reveal that social e-commerce leads to a 3.09∼10.37 times higher purchase conversion rate than the conventional settings, where users make purchases with significantly fewer item explorations. We propose and validate four primary mechanisms that contribute to the efficient purchase conversion: better matching, social enrichment, social proof, and price sensitivity. We identify several behavioral indicators that can measure these mechanisms' effectiveness, based on which we design an accurate predictive model (AUC=0.7738) for user's purchase decisions. These results combine to shed light on how to understand and model the purchase behavior in social e-commerce.
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Social e-commerce provides a novel "counter" case to the HCI4D and ICTD literature: contrary to prior mainstream technologies that the disadvantaged populations are generally more reluctant and experience more barriers in adoption and engagement, social e-commerce 1) first prospers among the traditionally underserved community from developing regions ahead of the more technologically advantaged communities, and 2) has been heavily engaged by this community. Through 12 in-depth interviews with social e-commerce users from the traditionally underserved community in Chinese developing regions, we demonstrate how social e-commerce, acting as a "virtual bazaar", achieves this. We identify that social e-commerce brings online the traditional offline socioeconomic lives the community has lived for ages, fits into the community's social, cultural, and economic context, and thus effectively promotes technology inclusivity. Our work provides novel insights and implications for building inclusive technology for the "next billion" population.
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On social e-commerce, agents are a key group of actors who enable market transactions. Social e-commerce transforms users into agents by motivating them with monetary rewards to promote products and invite new agents through their social network. Despite their rapid growth, there is still inadequate evidence on how such agent invitation works. This research examines what factors affect the agent invitation process. We first conduct a qualitative user study, where we identify four potential mechanisms affecting agent invitation: social conformity, social enrichment, refusal avoidance, and benefit-cost trade-off. Leveraging the empirical data collected from one of the largest social e-commerce platforms – Beidian, we operationalize a set of behavioral indicators of these mechanisms and further develop machine learning models to predict users' reactions to invitations. We find that the identified four mechanisms contribute to the high success rate of agent invitations differently. We conclude by discussing the implications of our findings and their potential benefits to real-world applications.
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On social e-commerce, agents act as intermediaries who connect producers with consumers by sharing information with and recommending products to their real-world social contacts. Despite their crucial role, the nature and behavior of these intermediaries on these social e-commerce platforms has not been systematically analyzed. Here we address this knowledge gap through a mixed-method study. Leveraging nine months' all-round behavior of about 40 million users on Beidian – one of the largest social e-commerce sites in China, alongside with qualitative evidence from online forums and interviews, we examine characteristics of intermediaries, identify their behavioral patterns, and uncover strategies and mechanisms that make successful intermediaries. We demonstrate that intermediaries on social e-commerce sites act as local trend detectors and "social grocers". Furthermore, successful intermediaries are highly dedicated whenever best sellers appear and broaden items for promotion. To the best of our knowledge, this paper presents the first large-scale analysis on the emerging role of intermediaries in social e-commerce platforms, which provides potential insights for the design and management of social computing marketing platforms.
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Social e-commerce, where human agents get involved in the recommendation feedback loop, offers a new solution to address the long-standing issue of recommendation homogeneity -- bringing friends' recommendations into the loop (friend-in-the-loop). In this paper, we conduct an exploratory study on the benefits of friend-in-the-loop through mixed methods on a leading social e-commerce platform in China, Beidian. We reveal that friend-in-the-loop provides users with more accurate and diverse recommendations than merely recommender systems, and significantly alleviates algorithmic homogeneity. Moreover, our qualitative results demonstrate that the introduction of friends' external knowledge, consumers' trust, and empathy accounts for these benefits. Overall, we elaborate that friend-in-the-loop comprehensively benefits both users and recommender systems, and it is a promising HCI-based solution to recommendation homogeneity, which offers insightful implications on designing future human-algorithm collaboration models.
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In social e-commerce, customers' purchase decisions and thus customer value can be influenced by social relationships. Considering this, we propose a novel framework — Motif-based Multi-view Graph Attention Networks with Gated Fusion (MAG), which jointly considers customer demographics, past behaviors, and social network structures. Specifically, (1) to make the best use of higher-order information in complex social networks, we design a motif-based multi-view graph attention module, which explicitly captures different higher-order structures, along with the attention mechanism auto-assigning high weights to informative ones. (2) To model the complex effects of customer attributes and social influence, we propose a gated fusion module with two gates: one depicts the susceptibility to social influence and the other depicts the dependency of the two factors. Extensive experiments on two large-scale datasets show superior performance of our model over the state-of-the-art baselines. Further, we discover that the increase of motifs does not guarantee better performances and identify how motifs play different roles. These findings shed light on how to understand socio-economic relationships among customers and find high-value customers.
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In social e-commerce, one of the most important problems is to predict the value of a community formed by closely connected users in social networks due to its tremendous business value. However, few works have addressed this problem because of 1) its novel setting and 2) its challenging nature that the structure of a community has complex effects on its value. To bridge this gap, we develop a Multi-scale Structure-aware Community value prediction network (MSC) that jointly models the structural information of different scales, including peer relations, community structure, and inter-community connections, to predict the value of given communities. Specifically, we first propose a Masked Edge Learning Graph Convolutional Network (MEL-GCN) based on a novel masked propagation mechanism to model peer influence. Then, we design a Pair-wise Community Pooling (PCPool) module to capture critical community structures. Finally, we model the inter-community connections by distinguishing intra-community edges from inter-community edges and employing a Multi-aggregator Framework (MAF). Extensive experiments on a large-scale real-world social e-commerce dataset demonstrate our method's superior performance over state-of-the-art baselines, with a relative performance gain of 11.40%, 10.01%, and 10.97% in MAE, RMSE, and NRMSE, respectively. Further ablation study shows the effectiveness of our designed components.
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In online services such as social e-commerce, accurate churn prediction for retaining users is keenly important because it determines their survival and prosperity. Recent research has specified social influence to be one of the most important reasons for user churn, and thereby many works start to model its effects on user churn to improve the prediction performance. However, existing works only use the data's correlational information while neglecting the problem's causal nature. To bridge this gap, we develop a counterfactual modeling framework for churn prediction, which can effectively capture the causal information of social influence for accurate and explainable churn predictions. Specifically, we first propose a backbone framework that uses two separate embeddings to model users' endogenous churn intentions and the exogenous social influence. Then, we propose a counterfactual data augmentation module to introduce the causal information to the model by providing partially labeled counterfactual data. Finally, we design a three-headed counterfactual prediction framework to guide the model to learn causal information to facilitate churn prediction. Extensive experiments on two large-scale datasets with different types of social relations show our model's superior prediction performance compared with the state-of-the-art baselines. We further conduct an in-depth analysis of the prediction results demonstrating our proposed method's ability to capture causal information of social influence and give explainable churn predictions, which provide insights into designing better user retention strategies.
Social e-commerce transforms a social community into an inclusive place to do business by enabling people to share products with their friends. We define this task of generating sharing suggestions as item recommendation for the word-of-mouth scenario, and to the best of our knowledge, this is a new task that has never been explored. In this paper, we propose a TriM (short for Triad based word-of-Mouth recommendation) model that can capture both the sharer's influence and the receiver's interest at the same time, which are two significant factors that determine whether the receiver will buy the product or not. Furthermore, with joint learning on two parts of interaction data to address the data sparsity issue, our proposed TriM-Joint further improves the recommendation performance. By conducting experiments, we show that our proposed models achieve the best results compared to state-of-the-art models with significant improvements by at least 7.4% ∼ 14.4%, respectively.
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Existing social recommendation methods violate the fact that users are likely to share preference on different products with different friends: friends' behaviors do not necessarily affect a user's preferences, and the influence is diverse among different items. In this paper, we contribute a new solution, CSR (short for Characterized Social Regularization) model by designing a universal regularization term for modeling variable social influence. This regularization term captures the finely grained similarity of social-connected friends. We further introduce two variants of our model with different optimization manners. Our proposed model can be applied to both explicit and implicit interaction due to its high generality. Extensive experiments on two real-world datasets demonstrate that our CSR significantly outperforms state-of-the-art social recommendation methods. Further experiments show that CSR can improve recommendation performance for those users with sparse social relations or behavioral interactions.
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On social e-commerce, there are numerous types of relations between three types of nodes, i.e., users, selling agents and items. The complex interactions in this type of social e-commerce can be formulated as Heterogeneous Information Networks (HIN). Although Graph Convolutional Networks (GCNs) have recently been established as the latest state-of-the-art methods in representation learning, prior GCN models have fundamental limitations in both modeling heterogeneous relations and efficiently sampling relevant receptive field from vast neighborhood. To address these problems, we propose RecoGCN, which stands for a RElation-aware CO-attentive GCN model, to effectively aggregate heterogeneous features in a HIN. It makes up current GCN's limitation in modeling heterogeneous relations with a relation-aware aggregator, and leverages the semantic-aware meta-paths to carve out concise and relevant receptive fields for each node. To effectively fuse the embeddings learned from different meta-paths, we further develop a co-attentive mechanism to dynamically assign importance weights to different meta-paths by attending the three-way interactions among users, selling agents and items. Extensive experiments on a real-world dataset demonstrate RecoGCN can learn meaningful node embeddings in HIN, and consistently outperforms baseline methods in recommendation tasks.
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In this paper, we address the problem of cross-platform recommendation for social e-commerce, i.e., recommending products to users when they are shopping through social media. Existing cross-platform and social-related recommendation methods cannot be applied directly for this problem since they do not co-consider the social information and the cross-platform characteristics together. To study this problem, we first investigate the heterogeneous shopping behaviors between traditional e-commerce app and social media. Based on these observations from data, we propose CROSS (Cross-platform Recommendation for Online Shopping in Social Media), a recommendation model utilizing not only user-item interaction data on both platforms, but also social relation data on social media. Extensive experiments on real-world online shopping dataset demonstrate that our proposed CROSS significantly outperforms existing state-of-the-art methods.
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Group buying, as an emerging form of purchase in social e-commerce websites, has recently achieved great success. Group-buying recommendation for social e-commerce, which recommends an item list when users want to launch a group, plays an important role in the group success ratio and sales. However, designing a personalized recommendation model for group buying is an entirely new problem that is seldom explored. In this work, we take the first step to approach the problem of group-buying recommendation for social e-commerce and develop a GBGCN method (short for Group-Buying Graph Convolutional Network). Considering the multiple types of behaviors and structured social network data, we first propose to construct directed heterogeneous graphs to represent behavioral data and social networks. We then develop a graph convolutional network model with multi-view embedding propagation, which can extract the complicated high-order graph structure to learn the embeddings. Last, since a failed group-buying implies rich preferences of the initiator and participants, we design a double-pairwise loss function to distill such preference signals. We collect a real-world dataset of group-buying and conduct experiments to evaluate the performance. Empirical results demonstrate that our proposed GBGCN can significantly outperform baseline methods by 2.69%-7.36%.