Journal ArticleDOI
Enhancing Top-N Recommendation Using Stacked Autoencoder in Context-Aware Recommender System
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TLDR
Experiments on four Amazon (5-core) datasets demonstrated that the proposed CSSAE model persistently outperforms state-of-the-art top-N recommendation methods on various effectiveness metrics.Abstract:
Context-aware recommender systems (CARS) are a vital module of many corporate, especially within the online commerce domain, where consumers are provided with recommendations about products potentially relevant for them. A traditional CARS, which utilizes deep learning models considers that user’s preferences can be predicted by ratings, reviews, demographics, etc. However, the feedback given by the users is often conflicting when comparing the rating score and the sentiment behind the reviews. Therefore, a model that utilizes either ratings or reviews for predicting items for top-N recommendation may generate unsatisfactory recommendations in many cases. In order to address this problem, this paper proposes an effective context-specific sentiment based stacked autoencoder (CSSAE) to learn the concrete preference of the user by merging the rating and reviews for a context-specific item into a stacked autoencoder. Hence, the user's preferences are consistently predicted to enhance the Top-N recommendation quality, by adapting the recommended list to the exact context where an active user is operating. Experiments on four Amazon (5-core) datasets demonstrated that the proposed CSSAE model persistently outperforms state-of-the-art top-N recommendation methods on various effectiveness metrics.read more
Citations
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The impact of online user reviews on hotel room sales [Summary]
TL;DR: Wang et al. as discussed by the authors developed a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings, which indicated a significant relationship between online consumer reviews and business performance of hotels.
Journal ArticleDOI
Regional spatio-temporal forecasting of particulate matter using autoencoder based generative adversarial network
S P Abirami,P. Chitra +1 more
TL;DR: A generative modeling approach is proposed to overcome the challenges in forecasting PM2.5 data by considering it as an ill-posed inverse problem and an Autoencoder-based generative adversarial network (GAN) named Air-GAN is developed.
Book ChapterDOI
Trust-Based Context-Aware Collaborative Filtering Using Denoising Autoencoder
S. Abinaya,M. K. Kavitha Devi +1 more
TL;DR: In this paper , the authors implemented the idea of denoising autoencoders for personalized context-aware recommendation, which comprises of split item rating according to all contextual conditions resulting in fictive items that is being fed into the denoizing autoencoder augmented with trust information to overcome sparsity.
Journal ArticleDOI
Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder
TL;DR: Wang et al. as discussed by the authors proposed a trust-based attentive contextual denoising autoencoder (TACDA) for enhanced Top-N context-aware recommendation, which takes the sparse preference of the user that is integrated with trust data as input into the auto-encoder to prevail over the cold start and sparsity obstacle and efficiently accumulates the context condition into the model via attention.
Journal ArticleDOI
Enhanced Visual Analytics Technique for Content-Based Medical Image Retrieval
S.K Abinaya,T. Rajasenbagam +1 more
TL;DR: In this article , the texture properties are retrieved using the shape features extraction and Gray Level Co-occurrence Matrix (GLCM) is performed by contour-based shape descriptor.
References
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Journal Article
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