92,782 research outputs found
Optical tomography: Image improvement using mixed projection of parallel and fan beam modes
Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be deļ¬ned by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The ļ¬ndings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam
Learning over Knowledge-Base Embeddings for Recommendation
State-of-the-art recommendation algorithms -- especially the collaborative
filtering (CF) based approaches with shallow or deep models -- usually work
with various unstructured information sources for recommendation, such as
textual reviews, visual images, and various implicit or explicit feedbacks.
Though structured knowledge bases were considered in content-based approaches,
they have been largely neglected recently due to the availability of vast
amount of data, and the learning power of many complex models.
However, structured knowledge bases exhibit unique advantages in personalized
recommendation systems. When the explicit knowledge about users and items is
considered for recommendation, the system could provide highly customized
recommendations based on users' historical behaviors. A great challenge for
using knowledge bases for recommendation is how to integrated large-scale
structured and unstructured data, while taking advantage of collaborative
filtering for highly accurate performance. Recent achievements on knowledge
base embedding sheds light on this problem, which makes it possible to learn
user and item representations while preserving the structure of their
relationship with external knowledge. In this work, we propose to reason over
knowledge base embeddings for personalized recommendation. Specifically, we
propose a knowledge base representation learning approach to embed
heterogeneous entities for recommendation. Experimental results on real-world
dataset verified the superior performance of our approach compared with
state-of-the-art baselines
STransE: a novel embedding model of entities and relationships in knowledge bases
Knowledge bases of real-world facts about entities and their relationships
are useful resources for a variety of natural language processing tasks.
However, because knowledge bases are typically incomplete, it is useful to be
able to perform link prediction or knowledge base completion, i.e., predict
whether a relationship not in the knowledge base is likely to be true. This
paper combines insights from several previous link prediction models into a new
embedding model STransE that represents each entity as a low-dimensional
vector, and each relation by two matrices and a translation vector. STransE is
a simple combination of the SE and TransE models, but it obtains better link
prediction performance on two benchmark datasets than previous embedding
models. Thus, STransE can serve as a new baseline for the more complex models
in the link prediction task.Comment: V1: In Proceedings of the 2016 Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies, NAACL HLT 2016. V2: Corrected citation to (Krompa{\ss} et al.,
2015). V3: A revised version of our NAACL-HLT 2016 paper with additional
experimental results and latest related wor
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
Word Embeddings for Entity-annotated Texts
Learned vector representations of words are useful tools for many information
retrieval and natural language processing tasks due to their ability to capture
lexical semantics. However, while many such tasks involve or even rely on named
entities as central components, popular word embedding models have so far
failed to include entities as first-class citizens. While it seems intuitive
that annotating named entities in the training corpus should result in more
intelligent word features for downstream tasks, performance issues arise when
popular embedding approaches are naively applied to entity annotated corpora.
Not only are the resulting entity embeddings less useful than expected, but one
also finds that the performance of the non-entity word embeddings degrades in
comparison to those trained on the raw, unannotated corpus. In this paper, we
investigate approaches to jointly train word and entity embeddings on a large
corpus with automatically annotated and linked entities. We discuss two
distinct approaches to the generation of such embeddings, namely the training
of state-of-the-art embeddings on raw-text and annotated versions of the
corpus, as well as node embeddings of a co-occurrence graph representation of
the annotated corpus. We compare the performance of annotated embeddings and
classical word embeddings on a variety of word similarity, analogy, and
clustering evaluation tasks, and investigate their performance in
entity-specific tasks. Our findings show that it takes more than training
popular word embedding models on an annotated corpus to create entity
embeddings with acceptable performance on common test cases. Based on these
results, we discuss how and when node embeddings of the co-occurrence graph
representation of the text can restore the performance.Comment: This paper is accepted in 41st European Conference on Information
Retrieva
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