The majority of recent work in latent Collaborative Filtering (CF) has focused on developing new model architectures to learn accurate user and item representations. Typically, a standard pairwise loss function (BPR, Triplet, etc.) is used in these models, and little exploration is done on how to optimally extract signals from the available preference information. In the implicit setting, negative examples are sampled, and these losses allocate weights that solely depend on the difference in user distance between observed (positive) and negative item pairs. This can ignore valuable global information from other users and items, and lead to sub-optimal results. Motivated by this problem, we propose a novel loss which first leverages mining to select the most informative pairs, followed by a weighing process to allocate more weight to harder examples. Our weighting process consists of four different components, and incorporates distance information from other users, enabling the model to better position the learned representations. We conduct extensive experiments and demonstrate that our loss can be applied to different types of CF models leading to significant gains with each type. In particular, by applying our loss to the graph convolutional architecture, we achieve new state-of-the-art results on four different datasets. Further analysis shows that through our loss the model is able to learn better user-item representation space compared to other losses. Full code for this work is available here: https://github.com/layer6ai-labs/MCL.

Machine Learning (ML) applications are proliferating in the enterprise. Relational data which are prevalent in enterprise applications are typically normalized; as a result, data has to be denormalized via primary/foreign-key joins to be provided as input to ML algorithms. In this paper, we study the implementation of popular nonlinear ML models, Gaussian Mixture Models (GMM) and Neural Networks (NN), over normalized data addressing both cases of binary and multiway joins over normalized relations. For the case of GMM, we show how it is possible to decompose computation in a systematic way both for binary joins and for multi-way joins to construct mixture models. We demonstrate that by factoring the computation, one can conduct the training of the models much faster compared to other applicable approaches, without any loss in accuracy. For the case of NN, we propose algorithms to train the network taking normalized data as the input. Similarly, we present algorithms that can conduct the training of the network in a factorized way and offer performance advantages. The redundancy introduced by denormalization can be exploited for certain types of activation functions. However, we demonstrate that attempting to explore this redundancy is helpful up to a certain point; exploring redundancy at higher layers of the network will always result in increased costs and is not recommended. We present the results of a thorough experimental evaluation, varying several parameters of the input relations involved and demonstrate that our proposals for the training of GMM and NN yield drastic performance improvements typically starting at 100%, which become increasingly higher as parameters of the underlying data vary, without any loss in accuracy.

Hyperbolic spaces offer a rich setup to learn embeddings with superior properties that have been leveraged in areas such as computer vision, natural language processing and computational biology. Recently, several hyperbolic approaches have been proposed to learn robust representations for users and items in the recommendation setting. However, these approaches don’t capture the higher order relationships that typically exist in the recommendation domain. Graph convolutional neural networks (GCNs) on the other hand excel at capturing higher order information by applying multiple levels of aggregation to local representations. In this paper we combine these frameworks in a novel way, by proposing a hyperbolic GCN model for collaborative filtering. We demonstrate that our model can be effectively learned with a margin ranking loss, and show that hyperbolic space has desirable properties under the rank margin setting. At test time, inference in our model is done using the hyperbolic distance which preserves the structure of the learned space. We conduct extensive empirical analysis on three public benchmarks and compare against a large set of baselines. Our approach achieves highly competitive results and outperforms leading baselines including the Euclidean GCN counterpart. We further study the properties of the learned hyperbolic embeddings and show that they offer meaningful insights into the data. Full code for this work is available here: https://github.com/layer6ai-labs/HGCF.

Collaborative filtering with implicit feedback is a ubiquitous class of recommendation problems where only positive interactions such as purchases or clicks are observed. Autoencoder-based recommendation models have shown strong performance on many implicit feedback benchmarks. However, these models tend to suffer from popularity bias making recommendations less personalized. User-generated reviews contain a rich source of preference information, often with specific details that are important to each user, and can help mitigate the popularity bias. Since not all reviews are equally useful, existing work has been exploring various forms of attention to distill relevant information. In the majority of proposed approaches, representations from implicit feedback and review branches are simply concatenated at the end to generate predictions. This can prevent the model from learning deeper correlations between the two modalities and affect prediction accuracy. To address these problems, we propose a novel Two-headed Attention Fused Autoencoder (TAFA) model that jointly learns representations from user reviews and implicit feedback to make recommendations. We apply early and late modality fusion which allows the model to fully correlate and extract relevant information from both input sources. To further combat popularity bias, we leverage the Noise Contrastive Estimation (NCE) objective to “de-popularize” the fused user representation via a two-headed decoder architecture. Empirically, we show that TAFA outperforms leading baselines on multiple real-world benchmarks. Moreover, by tracing attention weights back to reviews we can provide explanations for the generated recommendations and gain further insights into user preferences. Full code for this work is available here: https://github.com/layer6ai-labs/TAFA.

Machine Learning (ML) applications are proliferating in the enterprise. Increasingly enterprise data are used to build sophisticated ML models to assist critical business functions. Relational data which are prevalent in enterprise applications are typically normalized; as a result data have to be denormalized via primary/foreign-key joins to be provided as input to ML algorithms. In this paper we study the implementation of popular nonlinear ML models and in particular independent Gaussian Mixture Models (IGMM) over normalized data. For the case of IGMM we propose algorithms taking the statistical properties of the Gaussians into account to construct mixture models, factorizing the computation. In that way we demonstrate that we can conduct the training of the models much faster compared to other applicable approaches, without any loss in accuracy. We present the results of a thorough experimental evaluation, varying several parameters of the input relations involved and demonstrate that our proposals both for the case of IGMM yield drastic performance improvements which become increasingly higher as parameters of the underlying data vary, without any loss in accuracy.