TEXT EMBEDDINGS FOR CONTENT-BASED RECOMMENDATIONS AUTHORS
نویسندگان
چکیده
منابع مشابه
Concept-Based Document Recommendations for CiteSeer Authors
The information explosion in today’s electronic world has created the need for information filtering techniques that help users filter out extraneous content to identify the right information they need to make important decisions. Recommender systems are one approach to this problem, based on presenting potential items of interest to a user rather than requiring the user to go looking for them....
متن کاملTowards text-based recommendations
Recommender systems have become, like search engines, a tool that cannot be ignored by a website with a large selection of products, music, news or simply webpages. The performance of this kind of systems depends on a large amount of information. Meanwhile, the amount of information available in the Web is continuously growing. In this paper, we propose to provide recommendation from unstructur...
متن کاملText Segmentation based on Semantic Word Embeddings
We explore the use of semantic word embeddings [14, 16, 12] in text segmentation algorithms, including the C99 segmentation algorithm [3, 4] and new algorithms inspired by the distributed word vector representation. By developing a general framework for discussing a class of segmentation objectives, we study the effectiveness of greedy versus exact optimization approaches and suggest a new iter...
متن کاملIncorporating Metadata into Content-Based User Embeddings
Low-dimensional vector representations of social media users can benefit applications like recommendation systems and user attribute inference. Recent work has shown that user embeddings can be improved by combining different types of information, such as text and network data. We propose a data augmentation method that allows novel feature types to be used within off-the-shelf embedding models...
متن کاملRDF Graph Embeddings for Content-based Recommender Systems
Linked Open Data has been recognized as a useful source of background knowledge for building content-based recommender systems. Vast amount of RDF data, covering multiple domains, has been published in freely accessible datasets. In this paper, we present an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs...
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: Современные наукоемкие технологии (Modern High Technologies)
سال: 2018
ISSN: 1812-7320
DOI: 10.17513/snt.36944