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long tail

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

March 20, 2023 By Aleksi Aaltonen

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.

Tagged With: long tail, recommender systems

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

November 29, 2012 By Sunil Wattal

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

Tagged With: collaborative filtering, fragmentation, kartik hosanagar, long tail, personalization, recommender systems, wharton

Eric Clemons to speak on How Information Changes Consumer Behavior And How Consumer Behavior Determines Corporate Strategy

October 24, 2009 By Sunil Wattal

How Information Changes Consumer Behavior And How Consumer Behavior Determines Corporate Strategy

Eric Clemons

Professor of Operations and Information Management
The Wharton School
University of Pennsylvania

November 6, 2009

Alter Hall 405, 1000am – 1130am

Abstract

Information availability has increased consumers’ informedness, the degree to which they know what is available in the marketplace, with precisely which attributes and at precisely what price. This informedness has altered the demand side of market behavior: customers now discount more heavily when comparable products are available from competitors and when products do not meet their wants, needs, cravings and longings, but they no longer discount as heavily when purchasing unfamiliar products. Changes in the demand side are producing comparable changes in the supply side: firms earn less than their expectations when competing in traditional mass market fat spots, while earning far more than previously when entering newly created resonance marketing sweet spots. We trace the impact of hyperdifferentiation and resonance marketing on strategy, with a clear progression from a limited number of fat spots, through reliance upon line extensions, and ultimately to fully differentiated market sweet spots.

For a copy of the full paper, click here.

Tagged With: eric clemons, hyperdifferentiation, long tail, marketing strategy, online reviews, resonance marketing, trading up, wharton, word of mouth marketing

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