Complementary products recommendation is an important problem in e-commerce. Such recommendations increase the average order price and the number of products in baskets. Complementary products are typically inferred from basket data. In this study, we propose the BB2vec model. The BB2vec model learns vector representations of products by analyzing jointly two types of data - Baskets and Browsing sessions (visiting web pages of products). These vector representations are used for making complementary products recommendation. The proposed model alleviates the cold start problem by delivering better recommendations for products having few or no purchases. We show that the BB2vec model has better performance than other models which use only basket data.
. Modeling embeddings for exponential family distributions were presented in @cite_6 . The particular model of this family is Poisson embeddings" ( P-EMB ) which was applied to model quantities of products in market baskets. Vector representations of products learned by the P-EMB model can be used for inferring substitutable and complementary products. The application of exponential distributions is orthogonal to our research. We consider the model can be modified accordingly, i.e., learn several P-EMB models with partially shared parameters instead of prod2vec models.
@cite_6
2949447796 2963099342 1555163866 2170429532
Word embeddings are a powerful approach for capturing semantic similarity among terms in a vocabulary. In this paper, we develop exponential family embeddings, a class of methods that extends the idea of word embeddings to other types of high-dimensional data. As examples, we studied neural data with real-valued observations, count data from a market basket analysis, and ratings data from a movie recommendation system. The main idea is to model each observation conditioned on a set of other observations. This set is called the context, and the way the context is defined is a modeling choice that depends on the problem. In language the context is the surrounding words; in neuroscience the context is close-by neurons; in market basket data the context is other items in the shopping cart. Each type of embedding model defines the context, the exponential family of conditional distributions, and how the latent embedding vectors are shared across data. We infer the embeddings with a scalable algorithm based on stochastic gradient descent. On all three applications - neural activity of zebrafish, users' shopping behavior, and movie ratings - we found exponential family embedding models to be more effective than other types of dimension reduction. They better reconstruct held-out data and find interesting qualitative structure. The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions when the optimal parameter vector is sparse. This work characterizes a certain strong convexity property of general exponential families, which allows their generalization ability to be quantified. In particular, we show how this property can be used to analyze generic exponential families under L1 regularization. The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions, such as when there is some sparsity pattern of the optimal parameter. This work characterizes a certain strong convexity property of general exponential families, which allow their generalization ability to be quantified. In particular, we show how this property can be used to analyze generic exponential families under L_1 regularization. A procedure for modeling duration with some PDF (probability density function) or PMF (probability mass function) in the exponential family is presented. A means of selecting an appropriate member of the exponential family is suggested. The parameter estimation procedure presented here offers several advantages over other methods of duration modeling. First, the duration PMF can be found directly, rather than sampling and truncating the optimum density. Secondly, the optimum duration parameters are found from Ferguson's nonparametric PMF. This simplifies reestimation because the operation of casting a nonparametric PMF to the desired parametric family can be completely separated from the forward and backward algorithms. Thirdly, several competing members of the exponential family can be evaluated quickly for each state in the HMM. This makes it possible to model each state's duration with the best member from a set of parametric PMFs in the exponential family. Finally, the solution holds for an PDF or PMF in the exponential family, which includes a large number of promising candidates. >