Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation

Most of the research studies on recommender systems are focused on single-domain recommendations. With the growth of multi-domain internet stores such as iTunes, Google Play, and Amazon.com, an opportunity to offer recommendations across different domains become more and more attractive. But there are few research studies on cross-domain recommender systems. In this paper, we study both the cold-start problem and the hypothesis that cross-domain recommendations provide more accuracy using a large volume of user data from a true multi-domain recommender service. Our results indicate that cross-domain collaborative filtering could significantly improve the quality of recommendation in cold start context and the auxiliary profile size plays an important role in it. © 2013 Springer-Verlag

Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation

The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-clustering methods have successfully leveraged shared users and items across dense and sparse domains to improve inference quality. However, they rely on shared rating data and cannot scale to multiple sparse target domains (i.e., the one-to-many transfer setting). This, combined with the increasing adoption of neural recommender architectures, motivates us to develop scalable neural layer-transfer approaches for cross-domain learning. Our key intuition is to guide neural collaborative filtering with domain-invariant components shared across the dense and sparse domains, improving the user and item representations learned in the sparse domains. We leverage contextual invariances across domains to develop these shared modules, and demonstrate that with user-item interaction context, we can learn-to-learn informative representation spaces even with sparse interaction data. We show the effectiveness and scalability of our approach on two public datasets and a massive transaction dataset from Visa, a global payments technology company (19% Item Recall, 3x faster vs. training separate models for each domain). Our approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202

Domain Specific Deep Neural Network Model for Classification of Abnormalities on Chest Radiographs

This study collected pre-processed dataset of chest radiographs formulated a deep neural network model for detecting abnormalities It also evaluated the performance of the formulated model and implemented a prototype of the formulated model This was with the view to develop a deep neural network model to automatically classify abnormalities in chest radiographs In order to achieve the overall purpose of this research a large set of chest x-ray images were sourced for and collected from the CheXpert dataset which is an online repository of annotated chest radiographs compiled by the Machine Learning Research group Stanford University The chest radiographs were preprocessed into a format that can be fed into a deep neural network The preprocessing techniques used were standardization and normalization The classification problem was formulated as a multi-label binary classification model which used convolutional neural network architecture for making decision on whether an abnormality was present or not in the chest radiographs The classification model was evaluated using specificity sensitivity and Area Under Curve AUC score as parameter A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language The AUC ROC curve of the model was able to classify Atelestasis Support devices Pleural effusion Pneumonia A normal CXR no finding Pneumothorax and Consolidation However Lung opacity and Cardiomegaly had probability out of less than 0 5 and thus were classified as absent Precision recall and F1 score values were 0 78 this imply that the number of False Positive and False Negative are the same revealing some measure of label imbalance in the dataset The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absen

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