Exploring Deep Space: Learning Personalized Ranking in a Semantic Space

Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new opportunities for exploiting these two sources of information. To recommend items we propose to first learn a user-independent high-dimensional semantic space in which items are positioned according to their substitutability, and then learn a user-specific transformation function to transform this space into a ranking according to the user's past preferences. An advantage of the proposed architecture is that it can be used to effectively recommend items using either content that describes the items or user-item ratings. We show that this approach significantly outperforms state-of-the-art recommender systems on the MovieLens 1M dataset.Comment: 6 pages, RecSys 2016 RSDL worksho

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.Comment: 8 page

Directional Multivariate Ranking

User-provided multi-aspect evaluations manifest users' detailed feedback on the recommended items and enable fine-grained understanding of their preferences. Extensive studies have shown that modeling such data greatly improves the effectiveness and explainability of the recommendations. However, as ranking is essential in recommendation, there is no principled solution yet for collectively generating multiple item rankings over different aspects. In this work, we propose a directional multi-aspect ranking criterion to enable a holistic ranking of items with respect to multiple aspects. Specifically, we view multi-aspect evaluation as an integral effort from a user that forms a vector of his/her preferences over aspects. Our key insight is that the direction of the difference vector between two multi-aspect preference vectors reveals the pairwise order of comparison. Hence, it is necessary for a multi-aspect ranking criterion to preserve the observed directions from such pairwise comparisons. We further derive a complete solution for the multi-aspect ranking problem based on a probabilistic multivariate tensor factorization model. Comprehensive experimental analysis on a large TripAdvisor multi-aspect rating dataset and a Yelp review text dataset confirms the effectiveness of our solution.Comment: Accepted as a full research paper in KDD'2

On Sampling Strategies for Neural Network-based Collaborative Filtering

Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms pose extensive computational costs, making it challenging to scale and improve upon. In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework. We tackle this issue by first establishing a connection between the loss functions and the user-item interaction bipartite graph, where the loss function terms are defined on links while major computation burdens are located at nodes. We call this type of loss functions "graph-based" loss functions, for which varied mini-batch sampling strategies can have different computational costs. Based on the insight, three novel sampling strategies are proposed, which can significantly improve the training efficiency of the proposed framework (up to $\times 30$ times speedup in our experiments), as well as improving the recommendation performance. Theoretical analysis is also provided for both the computational cost and the convergence. We believe the study of sampling strategies have further implications on general graph-based loss functions, and would also enable more research under the neural network-based recommendation framework.Comment: This is a longer version (with supplementary attached) of the KDD'17 pape

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Prevalence of macropod progressive periodontal disease ("lumpy jaw") in wild western grey kangaroos (Macropus fuliginosus)

Macropod Progressive Periodontal Disease (MPPD), colloquially referred to as “lumpy jaw”, is a commonly observed disease in captive macropods. However, the prevalence of this disease in the wild is largely unknown. A systematic study of MPPD in wild macropods would provide an indication of the endemic presence of this disease in wild populations, and could assist those managing disease in captive populations, by highlighting potential risk factors for disease development. Utilising kangaroos culled as part of a population management program, this study used visual observation and computer tomography (CT) of skulls to investigate the prevalence of MPPD in wild western grey kangaroos (Macropus fuliginosus) from the Perth metropolitan region, Western Australia. The sample suitable for visual and CT analysis comprised 121 specimens, 71 (58.7%) male and 50 (41.3%) female, with the mean age for all 121 specimens being 4.5 years (±2.63 SD). No evidence of MPPD was detected in any of the specimens examined. Overabundance may not be associated with the development of MPPD, as previously considered, and age-related factors should not be eliminated. This results may reflect low susceptibility to MPPD in western grey kangaroos, given low prevalence is reported in this species in captive populations. Further investigation into species-specificity is recommended, and should include samples with soft tissue to improve sensitivity of disease detection. Surveillance of MPPD in wild populations of macropods helps to improve our understanding of the biological significance, development and potential spread of this disease. Notably, this information may assist in the management of MPPD in captive populations, and may have a positive impact on both the welfare and conservation of macropods in captivity

Adherence Patterns to Extended Cervical Screening Intervals in Women Undergoing HPV and Cytology Cotesting

Although guidelines have recommended extended interval cervical screening using concurrent human papillomavirus (HPV) and cytology (“cotesting”) for over a decade, little is known about its adoption into routine care. Using longitudinal medical record data (2003-2015) from Kaiser Permanente Northern California (KPNC), which adopted triennial cotesting in 2003, we examined adherence to extended interval screening. We analyzed predictors of screening intervals among 504,202 women undergoing routine screening, categorizing interval length into early

A retrospective study of macropod progressive periodontal disease ("lumpy jaw") in captive macropods across Australia and Europe: using data from the past to inform future macropod management

Macropod Progressive Periodontal Disease (MPPD) is a well-recognised disease that causes high morbidity and mortality in captive macropods worldwide. Epidemiological data on MMPD are limited, although multiple risk factors associated with a captive environment appear to contribute to the development of clinical disease. The identification of risk factors associated with MPPD would assist with the development of preventive management strategies, potentially reducing mortality. Veterinary and husbandry records from eight institutions across Australia and Europe were analysed in a retrospective cohort study (1995 to 2016), examining risk factors for the development of MPPD. A review of records for 2759 macropods found incidence rates (IR) and risk of infection differed between geographic regions and individual institutions. The risk of developing MPPD increased with age, particularly for macropods >10 years (Australia Incidence Rate Ratio (IRR) 7.63, p < 0.001; Europe IRR 7.38, p < 0.001). Prognosis was typically poor, with 62.5% mortality reported for Australian and European regions combined. Practical recommendations to reduce disease risk have been developed, which will assist zoos in providing optimal long-term health management for captive macropods and, subsequently, have a positive impact on both the welfare and conservation of macropods housed in zoos globally