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.
After invention of the word2vec model @cite_16 @cite_22 which is dedicated to learning of distributed word representations, it was rapidly extended to other kinds of data: sentences and documents @cite_25 ( doc2vec ), products in baskets @cite_3 ( prod2vec , bagged-prod2vec ), nodes in graph @cite_12 ( node2vec ), @cite_2 ( DeepWalk ), etc. The original word2vec algorithm in its skip-gram variant proved to solve the weighted matrix factorization problem, where the matrix consists of values of shifted PMI of words @cite_19 . The extension of the prod2vec for using items metadata was proposed in @cite_14 ( MetaProd2Vec ) and for the textual data in @cite_9 . Sequences of items selected by a user are modeled in by means of the word2vec -like model. User and item representations learned by their model are applied for making recommendations.
@cite_14 @cite_22 @cite_9 @cite_3 @cite_19 @cite_2 @cite_16 @cite_25 @cite_12
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Vector-based word representations can help to improve a document classifier.The information of word2vec vectors and bags of words are very complementary.The combination of word2vec and BOW word representations obtains the best results.Word2vec is much more stable than bag of words models in cross-domain experiments. In this paper we show how a vector-based word representation obtained via word2vec can help to improve the results of a document classifier based on bags of words. Both models allow obtaining numeric representations from texts, but they do it very differently. The bag of words model can represent documents by means of widely dispersed vectors in which the indices are words or groups of words. word2vec generates word level representations building vectors that are much more compact, where indices implicitly contain information about the context of word occurrences. Bags of words are very effective for document classification and in our experiments no representation using only word2vec vectors is able to improve their results. However, this does not mean that the information provided by word2vec is not useful for the classification task. When this information is used in combination with the bags of words, the results are improved, showing its complementarity and its contribution to the task. We have also performed cross-domain experiments in which word2vec has shown much more stable behavior than bag of words models. The word2vec model and application by have attracted a great amount of attention in recent two years. The vector representations of words learned by word2vec models have been shown to carry semantic meanings and are useful in various NLP tasks. As an increasing number of researchers would like to experiment with word2vec or similar techniques, I notice that there lacks a material that comprehensively explains the parameter learning process of word embedding models in details, thus preventing researchers that are non-experts in neural networks from understanding the working mechanism of such models. This note provides detailed derivations and explanations of the parameter update equations of the word2vec models, including the original continuous bag-of-word (CBOW) and skip-gram (SG) models, as well as advanced optimization techniques, including hierarchical softmax and negative sampling. Intuitive interpretations of the gradient equations are also provided alongside mathematical derivations. In the appendix, a review on the basics of neuron networks and backpropagation is provided. I also created an interactive demo, wevi, to facilitate the intuitive understanding of the model. We analyze skip-gram with negative-sampling (SGNS), a word embedding method introduced by , and show that it is implicitly factorizing a word-context matrix, whose cells are the pointwise mutual information (PMI) of the respective word and context pairs, shifted by a global constant. We find that another embedding method, NCE, is implicitly factorizing a similar matrix, where each cell is the (shifted) log conditional probability of a word given its context. We show that using a sparse Shifted Positive PMI word-context matrix to represent words improves results on two word similarity tasks and one of two analogy tasks. When dense low-dimensional vectors are preferred, exact factorization with SVD can achieve solutions that are at least as good as SGNS's solutions for word similarity tasks. On analogy questions SGNS remains superior to SVD. We conjecture that this stems from the weighted nature of SGNS's factorization. Since the invention of word2vec, the skip-gram model has significantly advanced the research of network embedding, such as the recent emergence of the DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms. Our analysis and proofs reveal that: (1) DeepWalk empirically produces a low-rank transformation of a network's normalized Laplacian matrix; (2) LINE, in theory, is a special case of DeepWalk when the size of vertices' context is set to one; (3) As an extension of LINE, PTE can be viewed as the joint factorization of multiple networks» Laplacians; (4) node2vec is factorizing a matrix related to the stationary distribution and transition probability tensor of a 2nd-order random walk. We further provide the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian. Finally, we present the NetMF method as well as its approximation algorithm for computing network embedding. Our method offers significant improvements over DeepWalk and LINE for conventional network mining tasks. This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.