Global and Local Tensor Factorization for Multi-criteria Recommender System
نویسندگان
چکیده
منابع مشابه
Multi-Criteria Recommender Systems
This chapter aims to provide an overview of the class of multi-criteria recommender systems. First, it defines the recommendation problem as a multicriteria decision making (MCDM) problem, and reviews MCDM methods and techniques that can support the implementation of multi-criteria recommenders. Then, it focuses on the category of multi-criteria rating recommenders – techniques that provide rec...
متن کاملA Multi-Criteria Recommender System For Tourism Destination
Today, the transmission of information on tourism through internet has been implemented through several systems, among of them are e-Tourism, tourism virtual reality mapping, tourism reservation system, location-based tourism services and tourism recommender system. Of all those varied systems, tourism recommender system plays awfully vital roles because the system is able to provide any touris...
متن کاملA new approach for building recommender system using non negative matrix factorization method
Nonnegative Matrix Factorization is a new approach to reduce data dimensions. In this method, by applying the nonnegativity of the matrix data, the matrix is decomposed into components that are more interrelated and divide the data into sections where the data in these sections have a specific relationship. In this paper, we use the nonnegative matrix factorization to decompose the user ratin...
متن کاملLogistic Tensor Factorization for Multi-Relational Data
Tensor factorizations have become increasingly popular approaches for various learning tasks on structured data. In this work, we extend the Rescal tensor factorization, which has shown state-of-the-art results for multi-relational learning, to account for the binary nature of adjacency tensors. We study the improvements that can be gained via this approach on various benchmark datasets and sho...
متن کاملTensor Factorization for Multi-relational Learning
Tensor factorization has emerged as a promising approach for solving relational learning tasks. Here we review recent results on a particular tensor factorization approach, i.e. Rescal, which has demonstrated state-of-the-art relational learning results, while scaling to knowledge bases with millions of entities and billions of known facts.
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: Patterns
سال: 2020
ISSN: 2666-3899
DOI: 10.1016/j.patter.2020.100023