Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

Wang, Jizhe; Huang, Pipei; Zhao, Huan; Zhang, Zhibo; Zhao, Binqiang; Lee, Dik Lun

doi:10.1145/3219819.3219869

Cited by 413 publications

(218 citation statements)

References 24 publications

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“…Recently, graph embedding based methods are applied in many industrial applications to complement or replace traditional methods. Wang et al [26] proposes to construct an item graph from users' behavior history and then applies the state-of-the-art graph embedding methods to learn the embedding of each item. To address the cold start and sparsity problem, they incorporate side information of items to enhance the embedding procedure.…”

Section: Related Work 21 Deep Matching In Industrymentioning

confidence: 99%

SDM

Jin

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

106

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Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching approaches in industry. However, they are not effective to model dynamic and evolving preferences of users. In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors. Compared with existing sequence-aware recommendation methods, we tackle the following two inherent problems in realworld applications: (1) there could exist multiple interest tendencies in one session.(2) long-term preferences may not be effectively fused with current session interests. Long-term behaviors are various and complex, hence those highly related to the short-term session should be kept for fusion. We propose to encode behavior sequences with two corresponding components: multi-head self-attention module to capture multiple types of interests and long-short term gated fusion module to incorporate long-term preferences. Successive items are recommended after matching between sequential user behavior vector and item embedding vectors. Offline experiments on real-world datasets show the superior performance of the proposed SDM. Moreover, SDM has been successfully deployed on online large-scale recommender system at Taobao and achieves improvements in terms of a range of commercial metrics.

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Section: Related Work 21 Deep Matching In Industrymentioning

confidence: 99%

SDM

Jin

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

106

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show abstract

“…[16] measured latent similarity between social images by metric learning. Wang et al used graph embedding techniques to find similar items [21]. Hsiao et al used the topic models to learn stylecoherent representations for fashion images retrieval [15].…”

Section: Related Work 21 Similarity Measurementmentioning

confidence: 99%

Measuring Similarity between Brands using Followers' Post in Social Media

Zhang

Wang

Sakai³

et al. 2019

Proceedings of the ACM Multimedia Asia

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In this paper, we propose a new measure to estimate the similarity between brands via posts of brands' followers on social network services (SNS). Our method was developed with the intention of exploring the brands that customers are likely to jointly purchase. Nowadays, brands use social media for targeted advertising because influencing users' preferences can greatly affect the trends in sales. We assume that data on SNS allows us to make quantitative comparisons between brands. Our proposed algorithm analyzes the daily photos and hashtags posted by each brand's followers. By clustering them and converting them to histograms, we can calculate the similarity between brands. We evaluated our proposed algorithm with purchase logs, credit card information, and answers to the questionnaires. The experimental results show that the purchase data maintained by a mall or a credit card company can predict the co-purchase very well, but not the customer's willingness to buy products of new brands. On the other hand, our method can predict the users' interest on brands with a correlation value over 0.53, which is pretty high considering that such interest to brands are high subjective and individual dependent.

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“…Network representation learning [10] has been widely studied and used in data mining community to support many applications such as social network mining [17] and recommendation systems [28,32]. Network representation learning can be either supervised by a downstream task, e.g., node classification for graph neural networks [14], or self-supervised by the adjacency relations, e.g., to approximate a certain proximity defined on the graph, as surveyed in [10].…”

Section: Introductionmentioning

confidence: 99%

Vertex-reinforced Random Walk for Network Embedding

Xiao

Zhao²,

Zheng³

et al. 2020

Proceedings of the 2020 SIAM International Conference on Data Mining

Self Cite

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In this paper, we study the fundamental problem of random walk for network embedding. We propose to use non-Markovian random walk, variants of vertex-reinforced random walk (VRRW), to fully use the history of a random walk path. To solve the getting stuck problem of VRRW, we introduce an exploitation-exploration mechanism to help the random walk jump out of the stuck set. The new random walk algorithms share the same convergence property of VRRW and thus can be used to learn stable network embeddings. Experimental results on two link prediction benchmark datasets and three node classification benchmark datasets show that our proposed approach reinforce2vec can outperform state-of-the-art random walk based embedding methods by a large margin.

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Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

Cited by 413 publications

References 24 publications

SDM

SDM

Measuring Similarity between Brands using Followers' Post in Social Media

Vertex-reinforced Random Walk for Network Embedding

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