打击过滤泡沫：大学课程推荐系统中的偶然性设计
Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System
摘要：
基于协同过滤的算法，包括递归神经网络（RNN），倾向于预测过去观察到的行为的延续。在推荐上下文中，这可能导致过于狭窄的建议集缺乏偶然性，并无意中将用户置于所谓的“过滤气泡”中。“在本文中，我们在公立大学的课程推荐系统的背景下，努力解决过滤气泡的问题。美国的大多数大学鼓励学生探索发展兴趣，同时建议他们坚持课程学习规范，使他们朝着毕业方向前进。这些相互竞争的目标，以及学生所涉及的利害关系，使得这种背景对于研究现实世界的推荐策略特别有意义。我们引入了一种新的修改skip-gram模型应用到九年的历史课程注册序列，学习课程向量表示用于多样化的建议，基于相似性的学生指定的最喜欢的课程。这个模型，我们称之为multifactor 2 vec，旨在通过学习令牌的潜在合并因子的嵌入来改善主令牌嵌入的语义（例如，讲师）。我们的离线测试发现，与标准skip-gram相比，该模型提高了我们的课程相似性和类比验证集的准确率和召回率。简化课程目录描述文本导致了进一步的改进。我们将这些模型的性能与系统现有的基于RNN的推荐进行了比较，并对本科生（N = 70）的用户研究进行了评价，以确定他们的课程推荐的六个特征。用户研究的结果显示，RNN推荐中缺乏新颖性，并描述了难以实现意外发现的特征权衡。
英文摘要：
Collaborative filtering based algorithms, including Recurrent Neural Networks (RNN), tend towards predicting a perpetuation of past observed behavior. In a recommendation context, this can lead to an overly narrow set of suggestions lacking in serendipity and inadvertently placing the user in what is known as a "filter bubble." In this paper, we grapple with the issue of the filter bubble in the context of a course recommendation system in production at a public university. Most universities in the United States encourage students to explore developing interests while simultaneously advising them to adhere to course taking norms which progress them towards graduation. These competing objectives, and the stakes involved for students, make this context a particularly meaningful one for investigating real-world recommendation strategies. We introduce a novel modification to the skip-gram model applied to nine years of historic course enrollment sequences to learn course vector representations used to diversify recommendations based on similarity to a student's specified favorite course. This model, which we call multifactor2vec, is intended to improve the semantics of the primary token embedding by also learning embeddings of potentially conflated factors of the token (e.g., instructor). Our offline testing found this model improved accuracy and recall on our course similarity and analogy validation sets over a standard skip-gram. Incorporating course catalog description text resulted in further improvements. We compare the performance of these models to the system's existing RNN-based recommendations with a user study of undergraduates (N = 70) rating six characteristics of their course recommendations. Results of the user study show a dramatic lack of novelty in RNN recommendations and depict the characteristic trade-offs that make serendipity difficult to achieve.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
