CHS: Small: Promoting Unexpected Information Discovery: An Interactive Framework for Computational Serendipity

Serendipity是一个与有价值的意外发现相关的概念。然而，大多数现有的推荐和信息检索算法，包括常用搜索工具和社交媒体使用的算法，都没有将意外发现作为选择显示内容的一个因素。相反，用于构建和评估这些算法的标准会奖励那些强化人们已经知道或相信的选择，而不是促进意想不到的、偶然的发现。这个项目的目标是在如何预测和培养人们在网上与信息互动时的意外发现方面取得进展。一个关键的挑战是如何衡量潜在的意外收获；要做到这一点，团队将创建捕获意外发现的重要子组件的度量，例如惊喜、价值和好奇心。另一个问题是如何开发算法，既能促进意外发现，又能满足人们对相关信息的需求；为此，该团队将创建算法来平衡相关性和偶然性，并开发出更好的模型，以反映用户的价值观和好奇心。这样做有望为推荐系统、信息检索和人机交互的研究做出贡献，同时开发真正的工具来支持图书馆的意外发现和知识发现。具体来说，研究人员将首先开发一个独立于应用领域的计算偶然性的交互式框架。该框架由四个部分组成：惊喜、价值、好奇心和序列组合。对于Surprise组件，我们假设当用户看到与他们的计算模型预测的期望相违背的物品时，他们会感到惊讶。我们将基于文本挖掘和深度学习技术的最新进展研究两个惊喜计算模型。惊喜组件将由价值组件来平衡，价值组件使用传统的协同过滤（CF）推荐方法来实现，以确保用户喜欢令人惊讶的物品。好奇元素是关于个性化的关卡和惊喜模式，能够激发用户的好奇心并维持他们的探索兴趣。序列组合组件综合了其他三个组件的输出，确定了多少惊喜足以让人接近，但又不会产生焦虑的感觉。输出是一系列的建议，引导用户走向越来越令人惊讶的概念，随着时间的推移，吸引他们的好奇心。用户反馈将被纳入更新惊喜、价值和好奇心组件，以发现新的意外发现序列。作为一个用例，研究人员将实施和评估框架，作为一个serendipity推荐器，SerenCat，旨在促进和研究学生在大学图书馆环境中对学习材料的发现。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Serendipity is a concept associated with unexpected discoveries that are valuable. However, most existing recommendation and information retrieval algorithms, including those used by common search tools and social media, do not include serendipity as an element they use to choose which content to show. Instead, the criteria used to build and evaluate these algorithms rewards choices that reinforce what people already know or believe, rather than promoting unexpected, serendipitous discoveries. This project's goal is to make progress on how to predict and foster serendipity when people interact with information online. One key challenge is how to measure potential serendipity; to do this, the team will create measures that capture important subcomponents of serendipity such as surprise, value, and curiosity. Another is how to develop algorithms that promote serendipity while still addressing people's needs for relevant information; toward this, the team will create algorithms that balance relevance and serendipity over time and that develop better models of the values and curiosity of the people who use them. Doing this promises to make contributions to the study of recommender systems, information retrieval, and human-computer interaction, along with developing real tools to support serendipity and knowledge discovery in libraries.Specifically, the investigators will first develop an interactive framework for computational serendipity that is independent of application domain. The framework consists of four components: Surprise, Value, Curiosity, and Sequence Composition. For the Surprise component, we hypothesize that users will be surprised when presented with items that violate their expectations as predicted by our computational model of them. We will study two computational models of surprise based on recent advances in text mining and deep learning techniques. The Surprise component will be balanced by a Value component, implemented with a traditional collaborative filtering (CF) recommender approach to ensure that the surprising item is liked by the user. The Curiosity component reasons about personalized levels and patterns of surprise that stimulate users' curiosity and sustain their interest to explore. The Sequence Composition component synthesizes the outputs from the other three components, identifying how much surprise is just surprising enough to be approachable, but not so much to generate anxious feelings. The output is a sequence of suggestions that guides the user toward increasingly surprising concepts, to engage their curiosity over time. User feedback will be incorporated to update the Surprise, Value, and Curiosity components for discovering new sets of serendipity sequences. As a use case, the investigators will implement and evaluate the framework as a serendipity recommender, SerenCat, designed to promote and study students' discovery in learning materials in a university library environment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.