Social networking services provide platforms for users to perform two kinds of behaviors: consumption behavior (e.g., recommending items of interest) and social link behavior (e.g., recommending potential social links). Accurately modeling and predicting users’ two kinds of behaviors are two core tasks in these platforms with various applications. Recently, with the advance of neural networks, many neural-based models have been designed to predict a single users’ behavior, i.e., social link behavior or consumption behavior. Compared to the classical shallow models, these neural-based models show better performance to drive a user’s behavior by modeling the complex patterns. However, there are few works exploiting whether it is possible to design a neural-based model to jointly predict users’ two kinds of behaviors to further enhance the prediction performance. In fact, social scientists have already shown that users’ two kinds of behaviors are not isolated; people trend to the consumption recommendation of friends on social platforms and would like to make new friends with like-minded users. While some previous works jointly model users’ two kinds of behaviors with shallow models, we argue that the correlation between users’ two kinds of behaviors are complex, which could not be well-designed with shallow linear models. To this end, in this article, we propose a neural joint behavior prediction model named Neural Joint Behavior Prediction Model (NJBP) to mutually enhance the prediction performance of these two tasks on social networking platforms. Specifically, there are two key characteristics of our proposed model: First, to model the correlation of users’ two kinds of behaviors, we design a fusion layer in the neural network to model the positive correlation of users’ two kinds of behaviors. Second, as the observed links in the social network are often very sparse, we design a new link-based loss function that could preserve the social network topology. After that, we design a joint optimization function to allow the two behaviors modeling tasks to be trained to mutually enhance each other. Finally, extensive experimental results on two real-world datasets show that our proposed method is on average 7.14% better than the best baseline on social link behavior while 6.21% on consumption behavior prediction. Compared with the pair-wise loss function on two datasets, our proposed link-based loss function improves at least 4.69% on the social link behavior prediction and 4.72% on the consumption behavior prediction.

中文翻译：

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社交网络平台用户行为预测的联合神经模型

社交网络服务为用户提供执行两种行为的平台：消费行为（例如，推荐感兴趣的项目）和社交链接行为（例如，推荐潜在的社交链接）。对用户的两种行为进行准确的建模和预测是这些具有各种应用程序的平台中的两个核心任务。最近，随着神经网络的进步，许多基于神经的模型被设计用来预测单个用户的行为，即社交链接行为或消费行为。与经典的浅层模型相比，这些基于神经的模型通过对复杂模式进行建模，在驱动用户行为方面表现出更好的性能。然而，很少有工作利用是否有可能设计一个基于神经的模型来联合预测用户的两种行为以进一步提高预测性能。事实上，社会科学家已经证明，用户的两种行为并不是孤立的；人们倾向于社交平台好友的消费推荐，希望结交志趣相投的新朋友。虽然之前的一些工作将用户的两种行为与浅层模型联合建模，但我们认为用户的两种行为之间的相关性很复杂，无法用浅层线性模型进行良好设计。为此，在本文中，我们提出了一种神经联合行为预测模型，命名为 人们倾向于社交平台好友的消费推荐，希望结交志趣相投的新朋友。虽然之前的一些工作将用户的两种行为与浅层模型联合建模，但我们认为用户的两种行为之间的相关性很复杂，无法用浅层线性模型进行良好设计。为此，在本文中，我们提出了一种神经联合行为预测模型，命名为 人们倾向于社交平台好友的消费推荐，希望结交志趣相投的新朋友。虽然之前的一些工作将用户的两种行为与浅层模型联合建模，但我们认为用户的两种行为之间的相关性很复杂，无法用浅层线性模型进行良好设计。为此，在本文中，我们提出了一种神经联合行为预测模型，命名为神经联合行为预测模型 (NJBP)以相互增强这两个任务在社交网络平台上的预测性能。具体来说，我们提出的模型有两个关键特征：首先，为了对用户两种行为的相关性进行建模，我们在神经网络中设计了一个融合层来对用户两种行为的正相关性进行建模。其次，由于社交网络中观察到的链接通常非常稀疏，我们设计了一种新的基于链接的损失函数，可以保留社交网络拓扑。之后，我们设计了一个联合优化函数，让两个行为建模任务得到训练，相互增强。最后，在两个真实世界数据集上的广泛实验结果表明，我们提出的方法平均比社交链接行为的最佳基线好 7.14%，而 6. 21% 的消费行为预测。与两个数据集上的成对损失函数相比，我们提出的基于链接的损失函数在社交链接行为预测上至少提高了 4.69%，在消费行为预测上提高了 4.72%。