A Two-Stage Neural Network-Based Cold Start Item Recommender
نویسندگان
چکیده
منابع مشابه
TrustRank: a Cold-Start tolerant recommender system
The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. Userand item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are...
متن کاملDesign a Hybrid Recommender System Solving Cold-start Problem Using Clustering and Chaotic PSO Algorithm
One of the main challenges of increasing information in the new era, is to find information of interest in the mass of data. This important matter has been considered in the design of many sites that interact with users. Recommender systems have been considered to resolve this issue and have tried to help users to achieve their desired information; however, they face limitations. One of the mos...
متن کاملAddressing the cold start problem in tag-based recommender systems
Folksonomies have become a powerful tool to describe, discover, search, and navigate online resources (e.g., pictures, videos, blogs) on the Social Web. Unlike taxonomies and ontologies, which impose a hierarchical categorisation on content, folksonomies directly allow end users to freely create and choose the categories (in this case, tags) that best describe a piece of information. However, t...
متن کاملPromoting Cold-Start Items in Recommender Systems
As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clea...
متن کاملDropoutNet: Addressing Cold Start in Recommender Systems
Latent models have become the default choice for recommender systems due to their performance and scalability. However, research in this area has primarily focused on modeling user-item interactions, and few latent models have been developed for cold start. Deep learning has recently achieved remarkable success showing excellent results for diverse input types. Inspired by these results we prop...
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: Applied Sciences
سال: 2021
ISSN: 2076-3417
DOI: 10.3390/app11094243