As an emerging online interaction service in Twitter-like social media systems, mention serves to significantly improve both user interaction experience and information propagation. In recent years, the problem of mentionee recommendation, i.e., recommending mentionees (mentioned users) when mentioners (mentioning users) mention others, has received considerable attention. However, the extreme sparsity of mentioner-mentionee matrix creates a severe challenge. While an increasing line of work has exploited diverse effects such as the textual content and spatio-temporal context influences to cope with this challenge, there lacks a comprehensive study of the joint effect of all these influencing factors. In light of this, we propose a joint latent-class probabilistic model, named Joint Topic-Area Model (JTAM), to tackle the mentionee recommendation problem by simultaneously learning and modeling users’ semantic interests, the spatio-temporal mentioning patterns of mentioners, the geographical distribution of mentionees, and their joint effects on users’ mention behaviors in a unified way. Moreover, to facilitate online query performance, we design an efficient query answering approach that enables fast top-k mentionee recommendation. To evaluate the performance of our method, we conduct extensive experiments on a large real-world dataset. The results demonstrate the superiority of our method in recommending mentionees in terms of both effectiveness and efficiency compared with other state-of-the-art methods.

作为类似Twitter的社交媒体系统中的新兴在线交互服务，请提及用于显着改善用户交互体验和信息传播。近年来，被提及者推荐的问题，即当提及者（提及用户）提及其他人时推荐被提及者（被提及的用户）已经受到相当大的关注。但是，提及者-提及者矩阵的极度稀疏性提出了严峻的挑战。尽管越来越多的工作已经利用各种效果（例如文本内容和时空背景影响）来应对这一挑战，但仍缺乏对所有这些影响因素共同作用的全面研究。有鉴于此，我们提出了一个联合潜在类概率模型，称为联合主题区域模型（JTAM），以通过同时学习和建模用户的语义兴趣来解决被提及者推荐问题，提及者的时空提及方式，提及者的地理分布及其对用户提及行为的共同影响。此外，为了提高在线查询的性能，我们设计了一种有效的查询回答方法，该方法可实现快速的顶部-k被推荐人推荐。为了评估我们方法的性能，我们在一个大型的真实数据集上进行了广泛的实验。结果表明，与其他最新方法相比，我们的方法在有效性和效率方面都具有推荐推荐对象的优势。