Predictability of diffusion-based recommender systems
نویسندگان
چکیده
منابع مشابه
Battling Predictability and Overconcentration in Recommender Systems
Today’s recommendation systems have evolved far beyond the initial approaches from more than a decade ago, and have seen a great deal of commercial success (c.f., Amazon and Netflix). However, they are still far from perfect, and face tremendous challenges in increasing the overall utility of the recommendations. These challenges are present in all stages of the recommendation process. They beg...
متن کاملKnowledge-based recommender systems
1. Introduction Recommender systems provide advice to users about items they might wish to purchase or examine. Recommendations made by such systems can help users navigate through large information spaces of product descriptions, news articles or other items. As on-line information and e-commerce burgeon, recommender systems are an increasingly important tool. A recent survey of recommender sy...
متن کاملCase-based Recommender Systems
In the past years, a number of research projects have focused on recommender systems. These systems implement various learning strategies to collect and induce user preferences over time and automatically suggest products that fit the learned user model. The most popular recommendation methodology is collaborative filtering (Resnick, Iacovou, Suchak, Bergstrom, & Riedl, 1994) that aggregates da...
متن کاملAgent-Based Recommender Systems
The main possibilities and challenges of agent-based recommender systems are examined.
متن کاملCase-based recommender systems
We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, coverin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Knowledge-Based Systems
سال: 2019
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2019.104921