LRMM: Learning to Recommend with Missing Modalities

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201

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

Life satisfaction in adolescents:the role of individual and social health assets

The aim of this study is to explore the relationship between adolescents’ life satisfaction and individual and social health assets. A nationally representative sample of 3,494 Portuguese adolescents (mean age = 14.94 ± 1.30 years; 53.6% girls) completed the Health Behavior in School-aged Children survey measuring a variety of health behaviors and beliefs. A sequential regression analysis was conducted with gender, individual assets (academic achievement, social competence, self-regulation and life objectives) and social assets (family support, peer support, parental monitoring and school connectedness) entered in separate steps. A second regression analysis was conducted with social assets entered before individual assets. The final model explained 18.3% of life satisfaction. School connectedness (β = .198, p < .001) and family support (β = .154, p < .001) were the strongest predictors of adolescents’ life satisfaction followed by social competence (β = .152, p < .001), academic achievement (β = .116, p < .001) and self-regulation (β = .064, p < .001). Social assets explained a larger variance of life satisfaction than individual assets when entered first in the regression (r2 = .134 and r2 = .119, respectively, p < .001). When entered last step in the regression analysis, social assets added more to life satisfaction’s variance than when individual assets were added in the last step (r2 = .060 and r2 = .045, respectively, p < .001). These results reinforce the role social interaction and social capital models in the promotion of well-being

The Impact of After-School Programs That Promote Personal and Social Skills

The first of several reports to come from CASEL's major meta-analysisproject. Conducted in collaboration with Joseph Durlak of Loyola Universityand funded by the W.T. Grant Foundation, this first report describes thestrong positive effects after-school programs can have, and the conditionsneeded to realize these benefits

Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis

Behavior prediction based on historical behavioral data have practical real-world significance. It has been applied in recommendation, predicting academic performance, etc. With the refinement of user data description, the development of new functions, and the fusion of multiple data sources, heterogeneous behavioral data which contain multiple types of behaviors become more and more common. In this paper, we aim to incorporate heterogeneous user behaviors and social influences for behavior predictions. To this end, this paper proposes a variant of Long-Short Term Memory (LSTM) which can consider context information while modeling a behavior sequence, a projection mechanism which can model multi-faceted relationships among different types of behaviors, and a multi-faceted attention mechanism which can dynamically find out informative periods from different facets. Many kinds of behavioral data belong to spatio-temporal data. An unsupervised way to construct a social behavior graph based on spatio-temporal data and to model social influences is proposed. Moreover, a residual learning-based decoder is designed to automatically construct multiple high-order cross features based on social behavior representation and other types of behavior representations. Qualitative and quantitative experiments on real-world datasets have demonstrated the effectiveness of this model