Recommender Systems Notation

As the field of recommender systems has developed, authors have used a myriad of notations for describing the mathematical workings of recommendation algorithms. These notations appear in research papers, books, lecture notes, blog posts, and software documentation. The disciplinary diversity of the field has not contributed to consistency in notation; scholars whose home base is in information retrieval have different habits and expectations than those in machine learning or human-computer interaction. In the course of years of teaching and research on recommender systems, we have seen the value in adopting a consistent notation across our work. This has been particularly highlighted in our development of the Recommender Systems MOOC on Coursera (Konstan et al. 2015), as we need to explain a wide variety of algorithms and our learners are not well-served by changing notation between algorithms. In this paper, we describe the notation we have adopted in our work, along with its justification and some discussion of considered alternatives. We present this in hope that it will be useful to others writing and teaching about recommender systems. This notation has served us well for some time now, in research, online education, and traditional classroom instruction. We feel it is ready for broad use

Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment

Recommender systems often struggle to strike a balance between matching users' tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users' preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don't like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interviews and surveys and found out that many users considered narrow recommendations to be useful, while a smaller number explicitly wanted greater breadth. Additionally, we designed and ran an online field experiment with a larger user group, evaluating two new interfaces designed to provide users with greater access to broader recommendations. We looked at user preferences and behavior for two groups of users: those with higher initial movie diversity and those with lower diversity. Among our findings, we discovered that different level of exploration control and users' subjective preferences on interfaces are more predictive of their satisfaction with the recommender.Comment: International Journal of Human Computer Interactio

User Choices and Regret: Understanding Users\u27 Decision Process about Consensually Acquired Spyware

Spyware is software which monitors user actions, gathers personal data, and/or displays advertisements to users. While some spyware is installed surreptitiously, a surprising amount is installed on users’ computers with their active participation. In some cases, users agree to accept spyware as part of a software bundle as a cost associated with gaining functionality they desire. In many other cases, however, users are unaware that they installed spyware, or of the consequences of that installation. This lack of awareness occurs even when the functioning of the spyware is explicitly declared in the end user license agreement (EULA). We argue and demonstrate that poor interface design contributes to the difficulty end users experience when trying to manage their computing environment. This paper reviews the legal, technical, and design issues related to the installation of spyware bundled with other software. It reports on results of an experiment in which thirty-one users were asked to configure computers, deciding which software to install from a set of software that included disclosed spyware. The results suggest that current EULA interfaces do little to encourage informed decision-making and that simpler interfaces with key terms highlighted have potential to improve informed decision-making

User Choices and Regret: Understanding Users\u27 Decision Process about Consensually Acquired Spyware

Spyware is software which monitors user actions, gathers personal data, and/or displays advertisements to users. While some spyware is installed surreptitiously, a surprising amount is installed on users’ computers with their active participation. In some cases, users agree to accept spyware as part of a software bundle as a cost associated with gaining functionality they desire. In many other cases, however, users are unaware that they installed spyware, or of the consequences of that installation. This lack of awareness occurs even when the functioning of the spyware is explicitly declared in the end user license agreement (EULA). We argue and demonstrate that poor interface design contributes to the difficulty end users experience when trying to manage their computing environment. This paper reviews the legal, technical, and design issues related to the installation of spyware bundled with other software. It reports on results of an experiment in which thirty-one users were asked to configure computers, deciding which software to install from a set of software that included disclosed spyware. The results suggest that current EULA interfaces do little to encourage informed decision-making and that simpler interfaces with key terms highlighted have potential to improve informed decision-making

Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models

A single digital newsletter usually contains many messages (regions). Users' reading time spent on, and read level (skip/skim/read-in-detail) of each message is important for platforms to understand their users' interests, personalize their contents, and make recommendations. Based on accurate but expensive-to-collect eyetracker-recorded data, we built models that predict per-region reading time based on easy-to-collect Javascript browser tracking data. With eye-tracking, we collected 200k ground-truth datapoints on participants reading news on browsers. Then we trained machine learning and deep learning models to predict message-level reading time based on user interactions like mouse position, scrolling, and clicking. We reached 27\% percentage error in reading time estimation with a two-tower neural network based on user interactions only, against the eye-tracking ground truth data, while the heuristic baselines have around 46\% percentage error. We also discovered the benefits of replacing per-session models with per-timestamp models, and adding user pattern features. We concluded with suggestions on developing message-level reading estimation techniques based on available data.Comment: Ruoyan Kong, Ruixuan Sun, Charles Chuankai Zhang, Chen Chen, Sneha Patri, Gayathri Gajjela, and Joseph A. Konstan. Getting the most from eyetracking: User-interaction based reading region estimation dataset and models. In Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, ETRA 23, New York, NY, USA, 2023. Association for Computing Machiner