John Riedl
ABSTRACT
Recommender systems have been revolutionizing the way shoppers and information seekers find what they want. We will study some of the tremendous successes and spectacular failures of recommenders in E-commerce to understand the causes of the success or failure. We will leverage that understanding into a set of principles for successfully applying recommenders to business problems. Finally, we will study the economic and social forces that are shaping the evolution of recommenders, and peer into the crystal ball to glimpse the directions the technology will be going in the future.
Index Terms
- Recommender systems in commerce and community
Recommendations
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
A survey of serendipity in recommender systems
We summarize most efforts on serendipity in recommender systems.We compare definitions of serendipity in recommender systems.We classify the state-of-the-art serendipity-oriented recommendation algorithms.We review methods to assess serendipity in ...
User Personality and User Satisfaction with Recommender Systems
In this study, we show that individual users' preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual ...
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