327 research outputs found
Low Dimensional Relevance Coding for Personalized Tag Recommendation in Image Tagging Applications
An approach of image coding for tag recommendation based on feature clustering and weighted coding is presented in this paper. The existing tag recommendation approach develops a decision based on correlation of image features and their tag annotated. The descriptive feature of the image sample defines the content of an image and is correlated with database features for tag recommendation. The feature dimension and its representation have a greater impact on the recommendation performance. The recent method tag recommendation developed CNN based visual features and proposed a tag recommendation based on weight factor. The dimensional feature and the isolated weight allocation limit the performance of presented tag recommendation system. This paper presents a new weight allocation and feature clustering method for tag recommendation. An approach of integral coding for weighted image-tag is presented to improve recommendation accuracy. The proposed recommendation system performance is tested on Flickr dataset for retrieval and recommendation accuracy
Image Tagging using Modified Association Rule based on Semantic Neighbors
With the rapid development of the internet, mobiles, and social image-sharing websites, a large number of images are generated daily. The huge repository of the images poses challenges for an image retrieval system. On image-sharing social websites such as Flickr, the users can assign keywords/tags to the images which can describe the content of the images. These tags play important role in an image retrieval system. However, the user-assigned tags are highly personalized which brings many challenges for retrieval of the images. Thus, it is necessary to suggest appropriate tags to the images.
Existing methods for tag recommendation based on nearest neighbors ignore the relationship between tags. In this paper, the method is proposed for tag recommendations for the images based on semantic neighbors using modified association rule. Given an image, the method identifies the semantic neighbors using random forest based on the weight assigned to each category. The tags associated with the semantic neighbors are used as candidate tags. The candidate tags are expanded by mining tags using modified association rules where each semantic neighbor is considered a transaction. In modified association rules, the probability of each tag is calculated using TF-IDF and confidence value.
The experimentation is done on Flickr, NUS-WIDE, and Corel-5k datasets. The result obtained using the proposed method gives better performance as compared to the existing tag recommendation methods
CDMF: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation
We proposes a novel deep neural network based recommendation model named Convolutional and Dense-layer Matrix Factorization (CDMF) for Context-aware recommendation, which is to combine multi-source information from item description and tag information. CDMF adopts a convolution neural network to extract hidden feature from item description as document and then fuses it with tag information via a full connection layer, thus generates a comprehensive feature vector. Based on the matrix factorization method, CDMF makes rating prediction based on the fused information of both users and items. Experiments on a real dataset show that the proposed deep learning model obviously outperforms the state-of-art recommendation methods
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
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
Spectral Collaborative Filtering
Despite the popularity of Collaborative Filtering (CF), CF-based methods are
haunted by the \textit{cold-start} problem, which has a significantly negative
impact on users' experiences with Recommender Systems (RS). In this paper, to
overcome the aforementioned drawback, we first formulate the relationships
between users and items as a bipartite graph. Then, we propose a new spectral
convolution operation directly performing in the \textit{spectral domain},
where not only the proximity information of a graph but also the connectivity
information hidden in the graph are revealed. With the proposed spectral
convolution operation, we build a deep recommendation model called Spectral
Collaborative Filtering (SpectralCF). Benefiting from the rich information of
connectivity existing in the \textit{spectral domain}, SpectralCF is capable of
discovering deep connections between users and items and therefore, alleviates
the \textit{cold-start} problem for CF. To the best of our knowledge,
SpectralCF is the first CF-based method directly learning from the
\textit{spectral domains} of user-item bipartite graphs. We apply our method on
several standard datasets. It is shown that SpectralCF significantly
outperforms state-of-the-art models. Code and data are available at
\url{https://github.com/lzheng21/SpectralCF}.Comment: RecSys201
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