MIS Distinguished Speaker Series

Temple University

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recommender systems

Mar 31 – Gedas Adomavicius – “Efficient and Flexible Long-Tail Recommendation Using Cosine Patterns”

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Time: Friday, 31 March 2023, 10:30–12:00
Room: LW420

Abstract

With the increasing use of recommender systems in various application domains, many algorithms have been proposed for improving the accuracy of recommendations. Among various other dimensions of recommender systems performance, long-tail (niche) recommendation performance remains an important challenge, due in large part to the popularity bias of many existing recommendation techniques. In this study, we propose CORE, a cosine-pattern-based technique, for effective long-tail recommendation. Comprehensive experimental results compare the proposed approach both to classical, widely-used recommendation algorithms and to specialized long-tail recommendation baselines, and demonstrate its practical benefits in accuracy, flexibility, and scalability, in addition to the superior long-tail recommendation performance.

Bio

Gedas Adomavicius is a professor in the Department of Information and Decision Sciences at the Carlson School of Management, University of Minnesota, where he also holds the Larson Endowed Chair for Excellence in Business Education. He received his PhD degree in computer science from New York University. His general research interests revolve around computational techniques for aiding decision-making in information-intensive environments and include personalization technologies and recommender systems, machine learning and data analytics, and electronic market mechanisms. His research has been published in a number of leading academic journals in information systems and computer science, including Information Systems Research, MIS Quarterly, Management Science, Journal of Operations Management, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, and Data Mining and Knowledge Discovery, and has been cited more than 28,000 times to date (according to Google Scholar). He has received several research grants from major funding institutions, including the U.S. National Science Foundation CAREER award for his research on personalization technologies. He has served on the editorial boards of several leading academic journals, including as Senior Editor for Information Systems Research and MIS Quarterly. In 2017, Prof. Adomavicius received the INFORMS Information Systems Society’s Distinguished Fellow Award. At the Carlson School of Management, he has taught analytics-related courses in the undergraduate, MBA, MSBA, PhD, and Executive Education programs and has served in several administrative roles, including as the chair of the Information and Decision Sciences Department.

Dec 3 – Jingjing Zhang to present “Longitudinal Impact of Preference Biases on Recommender Systems’ Performance”

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Longitudinal Impact of Preference Biases on Recommender Systems’ Performance

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Jingjing Zhang

Associate Professor
Fettig/Whirlpool Faculty Fellow
Department of Operations & Decision Technologies
Kelley School of Business
Indiana University

Friday, Dec 3

10:30am – 12 pm | Alter 603

Abstact:

Research studies have shown that recommender systems’ predictions that are observed by users can cause biases in users’ post-consumption preference ratings. Because users’ preference ratings are typically fed back to the system as training data for future predictions, this process is likely to influence the performance of the system in the long run. We use a simulation approach to study the longitudinal impact of preference biases (and their magnitude) on the dynamics of recommender systems’ performance. We look at the influence of preference biases in two conditions: (i) during the normal system use, where biases are typically caused by the system’s inherent prediction errors, and (ii) in the presence of external (deliberate) recommendation perturbations. Our simulation results show that preference biases significantly impair the system’s prediction performance (i.e., prediction accuracy) as well as users’ consumption outcomes (i.e., consumption relevance and diversity) over time. The impact is non-linear to the size of the bias, i.e., large bias causes disproportionately large negative effects. Also, items that are less popular and less distinctive (in terms of their content) are affected more by preference biases. Additionally, intentional recommendation perturbations, even on a small number of items for a short time, substantially amplify the negative impact of preference bias on a system’s longitudinal dynamics and causes long-lasting effects on users’ consumption. Our findings provide important implications for the design of recommender systems.

Dec 7: Kartik Hosanagar to present on Will the Global Village Fracture into Tribes: Recommender Systems and their Effects on Consumers

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Kartik Hosanagar
Associate Professor, OIM Dept
The Wharton School, University of Pennsylvania

December 7, 2012
Speakman Hall 200, 1000am – 1130am
Seminar Title : Will the Global Village Fracture into Tribes: Recommender Systems and their Effects on Consumers

Abstract
Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer’s preferences and recommend content best suited to him (e.g., “Customers who liked this also liked…”). A debate has emerged as to whether personalization has drawbacks. By making the web hyper-specific to our interests, does it fragment internet users, reducing shared experiences and narrowing media consumption? We study whether personalization is in fact fragmenting the online population. Surprisingly, it does not appear to do so in our study. Personalization appears to be a tool that helps users widen their interests, which in turn creates commonality with others. This increase in commonality occurs for two reasons, which we term volume and product mix effects. The volume effect is that consumers simply consume more after personalized recommendations, increasing the chance of having more items in common. The product mix effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations.

Please click here for a copy of the paper