1,359 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape
Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems
With the exponentially increasing volume of online data, searching and
finding required information have become an extensive and time-consuming task.
Recommender Systems as a subclass of information retrieval and decision support
systems by providing personalized suggestions helping users access what they
need more efficiently. Among the different techniques for building a
recommender system, Collaborative Filtering (CF) is the most popular and
widespread approach. However, cold start and data sparsity are the fundamental
challenges ahead of implementing an effective CF-based recommender. Recent
successful developments in enhancing and implementing deep learning
architectures motivated many studies to propose deep learning-based solutions
for solving the recommenders' weak points. In this research, unlike the past
similar works about using deep learning architectures in recommender systems
that covered different techniques generally, we specifically provide a
comprehensive review of deep learning-based collaborative filtering recommender
systems. This in-depth filtering gives a clear overview of the level of
popularity, gaps, and ignored areas on leveraging deep learning techniques to
build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure
Corporation robots
Nowadays, various robots are built to perform multiple tasks. Multiple robots working
together to perform a single task becomes important. One of the key elements for multiple
robots to work together is the robot need to able to follow another robot. This project is
mainly concerned on the design and construction of the robots that can follow line. In this
project, focuses on building line following robots leader and slave. Both of these robots will
follow the line and carry load. A Single robot has a limitation on handle load capacity such as
cannot handle heavy load and cannot handle long size load. To overcome this limitation an
easier way is to have a groups of mobile robots working together to accomplish an aim that
no single robot can do alon
A methodology for contextual recommendation using artificial neural networks
“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Recommender systems are an advanced form of software applications, more specifically
decision-support systems, that efficiently assist the users in finding items of their interest.
Recommender systems have been applied to many domains from music to e-commerce,
movies to software services delivery and tourism to news by exploiting available information
to predict and provide recommendations to end user. The suggestions generated by recommender
systems tend to narrow down the list of items which a user may overlook due to the
huge variety of similar items or users’ lack of experience in the particular domain of interest.
While the performance of traditional recommender systems, which rely on relatively simpler
information such as content and users’ filters, is widely accepted, their predictive capability
perfomrs poorly when local context of the user and situated actions have significant role in the
final decision. Therefore, acceptance and incorporation of context of the user as a significant
feature and development of recommender systems utilising the premise becomes an active
area of research requiring further investigation of the underlying algorithms and methodology.
This thesis focuses on categorisation of contextual and non-contextual features within
the domain of context-aware recommender system and their respective evaluation. Further,
application of the Multilayer Perceptron Model (MLP) for generating predictions and ratings
from the contextual and non-contextual features for contextual recommendations is presented
with support from relevant literature and empirical evaluation. An evaluation of specifically
employing artificial neural networks (ANNs) in the proposed methodology is also presented.
The work emphasizes on both algorithms and methodology with three points of consideration:\ud
contextual features and ratings of particular items/movies are exploited in several representations
to improve the accuracy of recommendation process using artificial neural networks
(ANNs), context features are combined with user-features to further improve the accuracy of
a context-aware recommender system and lastly, a combination of the item/movie features
are investigated within the recommendation process. The proposed approach is evaluated on
the LDOS-CoMoDa dataset and the results are compared with state-of-the-art approaches
from relevant published literature
Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems
This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. The learning process is based on the binary relevant/non-relevant vote and the binary voted/non-voted item information. This data reduction provides a new level of abstraction and it makes possible to design the classification-based architecture. In addition to the original architecture, its prediction process has a novel approach: it does not need to make a large number of predictions to get recommendations. Instead to run forward the neural network for each prediction, our approach runs forward the neural network just once to get a set of probabilities in its categorical output layer. The proposed neural architecture has been tested by using the MovieLens and FilmTrust datasets. A state-of-the-art baseline that outperforms current competitive approaches has been used. Results show a competitive recommendation quality and an interesting quality improvement on large number of recommendations, consistent with the architecture design. The architecture originality makes it possible to address a broad range of future works
A Personal Book Recommendation System Based on Brainwave Analysis
The recommendation system collects and analyzes users’ preferences, and recommend information or commodities to users automatically. In this research, we developed an online book recommendation system based on users’ brainwave information. We collected users’ brainwave information by electroencephalography (EEG) device, and applied empirical mode decomposition (EMD) to decompose the brainwave signal into intrinsic mode functions (IMFs). A back-propagation neural networks (BPNN) model was developed to portrait the user’s brainwave-preference correlations based on IMFs of brainwave signals, and it was applied to design and develop the recommendation system. This research has highlighted a research direction about human computer interaction (HCI) design about recommendation system
A Generative Adversarial Networks Based Approach for Literary Translation
This study aims to solve the problem of mistranslation due to the fact that literary intelligent translation only stays at the stage of text description and elaboration and lacks relevant facts. Therefore, this paper puts forward an improvement method of literary intelligent translation text based on generation confrontation network. First, an adaptive literary intelligent translation mode is designed under the generation confrontation network, and then the data of literary intelligent translation text improvement is preprocessed, and the data mining of text improvement quality evaluation is carried out. According to the mining results, a literary intelligent translation text improvement quality evaluation model is constructed to evaluate the quality of literary intelligent translation text improvement. According to the quality results, this paper constructs the improvement model of literary intelligent translation text, designs the improvement process, and completes the research on the improvement method of literary intelligent translation text that generates confrontation network. The experimental results show that this method has better detection effect of mistranslation features, better stability of the improved method, accurate and reliable results, and can improve the literary literacy of students and teachers
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