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

偶然发现是一个与有价值的意外发现有关的概念。 然而，大多数现有的推荐和信息检索算法，包括常见搜索工具和社交媒体使用的算法，都不包括意外发现作为他们用来选择显示哪些内容的元素。 相反，用于构建和评估这些算法的标准奖励那些强化人们已经知道或相信的选择，而不是促进意外的偶然发现。 该项目的目标是在人们与在线信息互动时如何预测和培养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之喜;为此，团队将创建衡量意外之喜的重要子组件，如惊喜，价值和好奇心。 另一个问题是如何开发算法，在满足人们对相关信息需求的同时促进意外发现;为此，团队将创建算法，随着时间的推移平衡相关性和意外发现，并开发更好的价值观模型和使用它们的人的好奇心。 这将有助于推荐系统、信息检索和人机交互的研究，沿着有助于开发支持图书馆中的偶然发现和知识发现的真实的工具。具体来说，研究人员将首先开发一个独立于应用领域的交互式计算偶然发现框架。该框架由四个部分组成：惊喜，价值，电流和序列组成。对于惊喜组件，我们假设用户会感到惊讶时，提出的项目，违反了他们的期望，我们的计算模型预测他们。我们将基于文本挖掘和深度学习技术的最新进展来研究两种惊喜计算模型。惊喜组件将由价值组件平衡，该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项目。Curriculum组件对个性化的惊喜级别和模式进行推理，以激发用户的好奇心并保持他们探索的兴趣。序列构成部分综合了其他三个部分的输出，确定了多少惊喜只是足够让人感到惊讶，但又不至于让人产生焦虑感。输出是一系列建议，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。 将纳入用户反馈，以更新惊喜，价值和Currency组件，以发现新的意外发现序列集。作为一个用例，研究人员将实施和评估框架作为一个偶然的推荐，SerenCat，旨在促进和研究学生的学习材料在大学图书馆环境中的发现。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.