Using AI to build communities around interests on LinkedIn

Abdulla Al-Qawasmeh, Ankan Saha
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Abstract

At LinkedIn, our mission is to connect the world's professionals to make them more productive and successful. Our team, Communities Artificial Intelligence (AI), at LinkedIn helps our members achieve this goal is by providing a platform where communities can form around common interests and shared experiences. Fostering active communities at LinkedIn can be broken down into the following components: (1) Discover: Help members find new entities (members, companies, hashtags, and more) to follow that will expose them to communities that share their interests. (2) Engage: Engage members in the conversations taking place in their communities by recommending content from their areas of interest. (3) Contribute: Help members effectively engage with the right communities when they create or share content. These three components form the main pillars of a content-driven ecosystem and our goal is to use AI to successfully close the loop between Discover (via providing relevant follow recommendations), Engage (via delivering engaging content to users from their areas of interest), and Contribute (via suggesting hashtags to content creators to target the right audience). A diverse set of AI techniques is required to address the challenges that arise in each of these components. These techniques include: Supervised Learning (XGBoost, Logistic Regression, Linear Regression), Wide and Deep Models, Natural Language Processing (e.g., Word Embeddings, ngram matching), and Unsupervised Learning. In this presentation, we will provide an overview of the AI techniques we use to form active communities on LinkedIn. We will describe two solutions in detail. First, we will describe how we have built our Follow Recommendations product. The goal of the Follow Recommendations product is to recommend entities to a member that the member finds both immediately relevant (i.e., increase the probability the member will follow the recommended entity) as well as engaging in the long run (i.e., the recommended entity produces content that the member finds relevant). Our analysis of the performance of our follow recommendations models has shown the superiority of nonlinear models compared to their linear counterparts. To manage the explosion of data emanating from terabytes of features generated from (viewer, entity) pairs, we use an innovative 2-D hash join algorithm that was developed at LinkedIn. We are also moving towards a hybrid scoring architecture. This allows us to score candidates with complex offline models and then re-rank these candidates based on more time-sensitive contextual features online. This generates more relevant and timely recommendations for the members based on their recent activity on different parts of the LinkedIn ecosystem. Second, we will describe our approach to solve the problem of Hashtag Suggestion and Typeahead. Hashtags are a great tool that allows members to expand the reach of their posts to the right audience (or communities). Our Hashtag Suggestion and Typeahead (HST) product was built to aid members in adding hashtags to their posts. We do not only recommend hashtags that the member is likely to select into their post, but also hashtags that are more likely to get the member the most online feedback. We call the latter aspect downstream utility (or engagement). However, before realizing this utility, the member has to actually select from the recommended hashtags. Therefore, the HST product is produced by combining two models. The first model maximizes the probability that the member will select the suggested hashtag and the second one optimizes for downstream utility. Based on content consumption behavior on LinkedIn, we have a good understanding of the supply and demand of content tagged with a specific hashtag. This information enables us to shape the inventory as well as traffic in individual hashtag domains, thus providing a better experience to content-starved communities.
利用人工智能在领英上建立兴趣社区
在领英,我们的使命是连接世界上的专业人士,使他们更有效率,更成功。我们的团队,LinkedIn的社区人工智能(AI),通过提供一个平台来帮助我们的成员实现这一目标,在这个平台上,社区可以围绕共同的兴趣和共享的经历形成。在LinkedIn上培养活跃的社区可以分为以下几个部分:(1)发现:帮助会员找到新的实体(成员、公司、标签等)来关注,这将使他们接触到与他们有共同兴趣的社区。(2)参与:通过推荐他们感兴趣的领域的内容,让成员参与到他们社区中发生的对话中。(3)贡献:帮助成员在创建或分享内容时有效地与适当的社区互动。这三个组成部分构成了内容驱动生态系统的主要支柱,我们的目标是使用人工智能成功地完成Discover(通过提供相关的关注建议),Engage(通过向用户提供他们感兴趣的领域的吸引人的内容)和Contribute(通过向内容创作者建议标签以瞄准正确的受众)之间的循环。需要一套多样化的人工智能技术来解决这些组件中出现的挑战。这些技术包括:监督学习(XGBoost,逻辑回归,线性回归),宽深度模型,自然语言处理(例如,词嵌入,图像匹配)和无监督学习。在本次演讲中,我们将概述我们用于在LinkedIn上形成活跃社区的人工智能技术。我们将详细描述两种解决方案。首先,我们将描述我们是如何构建Follow Recommendations产品的。Follow Recommendations产品的目标是向成员推荐那些成员发现立即相关的实体(即,增加成员关注被推荐实体的概率)以及长期参与的实体(即,被推荐的实体产生成员发现相关的内容)。我们对跟踪推荐模型的性能分析表明,与线性模型相比,非线性模型具有优越性。为了管理从(查看器,实体)对生成的tb级特征中产生的数据爆炸,我们使用了LinkedIn开发的创新二维哈希连接算法。我们也在朝着混合得分架构发展。这使我们能够使用复杂的离线模型对候选人进行评分,然后根据对时间更敏感的在线上下文特征对这些候选人进行重新排名。这会根据会员最近在LinkedIn生态系统不同部分的活动,为他们提供更相关、更及时的推荐。其次,我们将描述我们解决Hashtag Suggestion和Typeahead问题的方法。话题标签是一个很好的工具,它允许成员将他们的帖子扩展到合适的受众(或社区)。我们的标签建议和提前输入(HST)产品旨在帮助会员在他们的帖子中添加标签。我们不仅会推荐会员可能会选择的标签,还会推荐那些更有可能让会员获得最多在线反馈的标签。我们将后一个方面称为下游效用(或约定)。但是,在实现此实用程序之前,成员必须实际从推荐的hashtag中进行选择。因此,HST产品是由两种模型结合而成。第一个模型最大化成员选择建议的hashtag的概率,第二个模型针对下游效用进行优化。基于LinkedIn上的内容消费行为,我们很好地理解了带有特定标签的内容的供求关系。这些信息使我们能够塑造单个标签域的库存和流量,从而为内容匮乏的社区提供更好的体验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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