Includes bibliographical references.

Session-based Hotel Recommendations Dataset: As part of the ACM Recommender System Challenge 2019 -- Industrial Federated Topic Modeling -- A Novel Multi-task tensor Correlation Neural Network for Facial Attribute Prediction -- Self-weighted Robust LDA for Multiclass Classification with Edge Classes -- Bayesian Nonparametric Unsupervised Unit Detection for Data Stream Mining -- BiNeTClus: Bipartite Network Community Detection Based on Transactional Clustering -- CSL+ : Scalable Collective Subjective Logic Under Multidimensional Uncertainty -- Deep Energy Factorization model for Demographic Prediction -- Deep Learning Thermal Image Translation for Night Vision Perception -- A Theoretical Revisit to Linear Convergence for Saddle Point Problems -- On Representation Learning for Road Networks -- Uncovering Media Bias via Social Network Learning --Pricing-aware Real time Charging Scheduling and Charging Station Expansion for large-scale Electric Buses.

[Article Title: Session-based Hotel Recommendations Dataset: As part of the ACM Recommender System Challenge 2019/ Jens Adamczak, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Peter Knees, Gerard-Paul Leyson, and Philipp Monreal, p. 1:1-1:20]

Abstract: In 2019, the Recommender Systems Challenge [17] dealt for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset that we believe is vitally important for recommendation systems research in the area of hotel search, from both academic and industry perspectives. In this article, we describe the qualitative characteristics of the dataset and present the comparison of several baseline algorithms trained on the data.

https://doi.org/10.1145/3412379

[Article Title: Industrial Federated Topic Modeling/ D. Jiang, Y. Tong, Y. Song, X. Wu, W Zhao, J. Peng, R. Lian, Q. Xu, Q. Yang, p. 2:1-2:22]

Abstract: Probabilistic topic modeling has been applied in a variety of industrial applications. Training a high-quality model usually requires a massive amount of data to provide comprehensive co-occurrence information for the model to learn. However, industrial data such as medical or financial records are often proprietary or sensitive, which precludes uploading to data centers. Hence, training topic models in industrial scenarios using conventional approaches faces a dilemma: A party (i.e., a company or institute) has to either tolerate data scarcity or sacrifice data privacy. In this article, we propose a framework named Industrial Federated Topic Modeling (iFTM), in which multiple parties collaboratively train a high-quality topic model by simultaneously alleviating data scarcity and maintaining immunity to privacy adversaries. iFTM is inspired by federated learning, supports two representative topic models (i.e., Latent Dirichlet Allocation and SentenceLDA) in industrial applications, and consists of novel techniques such as private Metropolis-Hastings, topic-wise normalization, and heterogeneous model integration. We conduct quantitative evaluations to verify the effectiveness of iFTM and deploy iFTM in two real-life applications to demonstrate its utility. Experimental results verify iFTM’s superiority over conventional topic modeling.

https://doi.org/10.1145/3418283

[Article Title: A Novel Multi-task Tensor Correlation Neural Network for Facial Attribute Prediction/ Mingxing Duan, Kenli Li, Keqin Li, and Qi Tian, p. 3:1-3:22]

Abstract: Multi-task learning plays an important role in face multi-attribute prediction. At present, most researches excavate the shared information between attributes by sharing all convolutional layers. However, it is not appropriate to treat the low-level and high-level features of the face multi-attribute equally, because the high-level features are more biased toward the specific content of the category. In this article, a novel multi-attribute tensor correlation neural network (MTCN) is used to predict face attributes. MTCN shares all attribute features at the low-level layers, and then distinguishes each attribute feature at the high-level layers. To better excavate the correlations among high-level attribute features, each sub-network explores useful information from other networks to enhance its original information. Then a tensor canonical correlation analysis method is used to seek the correlations among the highest-level attributes, which enhances the original information of each attribute. After that, these features are mapped into a highly correlated space through the correlation matrix. Finally, we use sufficient experiments to verify the performance of MTCN on the CelebA and LFWA datasets and our MTCN achieves the best performance compared with the latest multi-attribute recognition algorithms under the same settings. https://doi.org/10.1145/3418285

[Article Title: Self-weighted Robust LDA for Multiclass Classification with Edge Classes/ Caixia Yan, Xiaojun Chang, Minnan Luo, Qinghua Zheng, Xiaoqin Zhang, Zhihui Li, and Feiping Nie, p. 4:1-4:19]

Abstract: Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the others, i.e., edge class, which occurs frequently in multi-class classification. First, the existence of edge classes often makes the total mean biased in the calculation of between-class scatter matrix. Second, the exploitation of ℓ2-norm based between-class distance criterion magnifies the extremely large distance corresponding to edge class. In this regard, a novel self-weighted robust LDA with ℓ2,1-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes. SWRLDA can automatically avoid the optimal mean calculation and simultaneously learn adaptive weights for each class pair without setting any additional parameter. An efficient re-weighted algorithm is exploited to derive the global optimum of the challenging ℓ2,1-norm maximization problem. The proposed SWRLDA is easy to implement and converges fast in practice. Extensive experiments demonstrate that SWRLDA performs favorably against other compared methods on both synthetic and real-world datasets while presenting superior computational efficiency in comparison with other techniques.

https://doi.org/10.1145/3418284