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

Automatically building research reading lists

All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting exist-ing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of cita-tions. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node’s im-portance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evalua-tion, including both an offline analysis and a user study, of the performance of the algorithms. Results from these stud-ies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists

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

Better supporting workers in ML workplaces

This workshop is aimed at bringing together a multidisciplinary group to discuss Machine Learning and its application in the workplace as a practical, everyday work matter. It's our hope this is a step toward helping us design better technology and user experiences to support the accomplishment of that work, while paying attention to workplace context. Despite advancement and investment in Machine Learning (ML) business applications, understanding workers in these work contexts have received little attention. As this category experiences dramatic growth, it's important to better understand the role that workers play, both individually and collaboratively, in a workplace where the output of prediction and machine learning is becoming pervasive. There is a closing window of opportunity to investigate this topic as it proceeds toward ubiquity. CSCW and HCI offer concepts, tools and methodologies to better understand and build for this future

The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction

Non peer reviewe

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

Non peer reviewe

Relation of exaggerated cytokine responses of CF airway epithelial cells to PAO1 adherence

In many model systems, cystic fibrosis (CF) phenotype airway epithelial cells in culture respond to P. aeruginosa with greater interleukin (IL)-8 and IL-6 secretion than matched controls. In order to test whether this excess inflammatory response results from the reported increased adherence of P. aeruginosa to the CF cells, we compared the inflammatory response of matched pairs of CF and non CF airway epithelial cell lines to the binding of GFP-PAO1, a strain of pseudomonas labeled with green fluorescent protein. There was no clear relation between GFP-PAO1 binding and cytokine production in response to PAO1. Treatment with exogenous aGM1 resulted in greater GFP-PAO1 binding to the normal phenotype compared to CF phenotype cells, but cytokine production remained greater from the CF cell lines. When cells were treated with neuraminidase, PAO1 adherence was equalized between CF and nonCF phenotype cell lines, but IL-8 production in response to inflammatory stimuli was still greater in CF phenotype cells. The polarized cell lines 16HBEo-Sense (normal phenotype) and Antisense (CF phenotype) cells were used to test the effect of disrupting tight junctions, which allows access of PAO1 to basolateral binding sites in both cell lines. IL-8 production increased from CF, but not normal, cells. These data indicate that increased bacterial binding to CF phenotype cells cannot by itself account for excess cytokine production in CF airway epithelial cells, encourage investigation of alternative hypotheses, and signal caution for therapeutic strategies proposed for CF that include disruption of tight junctions in the face of pseudomonas infection