Uncovering the information core in recommender systems
This addresses efficiency issues in recommender systems for online platforms by focusing on user contributions, though it is incremental as it builds on existing recommendation methods.
The paper tackles the problem of improving recommendation efficiency by identifying core users who carry most of the information, achieving 90% accuracy with only 20% of the data.
With the rapid growth of the Internet and overwhelming amount of information that people are confronted with, recommender systems have been developed to effiectively support users' decision-making process in online systems. So far, much attention has been paid to designing new recommendation algorithms and improving existent ones. However, few works considered the different contributions from different users to the performance of a recommender system. Such studies can help us improve the recommendation efficiency by excluding irrelevant users. In this paper, we argue that in each online system there exists a group of core users who carry most of the information for recommendation. With them, the recommender systems can already generate satisfactory recommendation. Our core user extraction method enables the recommender systems to achieve 90% of the accuracy by taking only 20% of the data into account.