Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation

Huang, Jin; Zhang, Ren; Zhao, Wayne Xin; He, Gaole; Wen, Ji-Rong; Dong, Daxiang

doi:10.1145/3289600.3290972

Cited by 62 publications

(35 citation statements)

References 43 publications

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“…However, user-item interaction data is usually sparse. To tackle the data sparsity problem, many techniques have been developed by utilizing the side information of items, such as review and taxonomy data [1,9,30]. As a comparison, CRS mainly focuses on the recommendation setting through conversation instead of historical interaction data.…”

Section: Related Workmentioning

confidence: 99%

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

Zhou

Zhao

Bian

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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191

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Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference. To address these issues, we incorporate both word-oriented and entity-oriented knowledge graphs (KG) to enhance the data representations in CRSs, and adopt Mutual Information Maximization to align the word-level and entity-level semantic spaces. Based on the aligned semantic representations, we further develop a KGenhanced recommender component for making accurate recommendations, and a KG-enhanced dialog component that can generate informative keywords or entities in the response text. Extensive experiments have demonstrated the effectiveness of our approach in yielding better performance on both recommendation and conversation tasks. CCS CONCEPTS • Information systems → Recommender systems; • Computing methodologies → Natural language generation.

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Section: Related Workmentioning

confidence: 99%

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

Zhou

Zhao

Bian

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

Self Cite

191

219

View full text Add to dashboard Cite

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“…Chen et al [3] introduces the memory mechanism to sequential recommender systems, which designs a user memory-augmented neural network (MANN) to express feature-level interests. As for more fine-grained user preference, Huang et al [8,9] use knowledge base information to enhance the semantic representation of key-value memory network called knowledge enhanced sequential recommender. However, the extra storage, manual feature design and computation of memory network of these methods cannot be accepted in industry on account of the large-scale users and items.…”

Section: Sequence-aware Recommendationmentioning

confidence: 99%

“…We choose the last hidden output h u t of LSTM as the query vector in multi-head attention to get the weights attending to [h u 1 , ..., h u t ]. The weight vector is also the tth row vector of A u i in Equation 8. head 1 and head 2 mainly concentrate on the first several items in the session, which are white down jackets.…”

Section: The Effect Of Multi-head Attentionmentioning

confidence: 99%

SDM

Jin

et al. 2019

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

100

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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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“…Taxonomies, the tree-structured hierarchies that represent the hypernymy (Is-A) relations, have been widely used in different domains, such as information extraction [5], question answering [35], and recommender systems [9], for the organization of concepts and instances as well as the injection of structured knowledge in downstream tasks. In particular, online catalog taxonomies serve Figure 1: The most relevant taxonomy nodes are shown on the left when a user searches "k cups" on Amazon.com.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, online catalog taxonomies serve Figure 1: The most relevant taxonomy nodes are shown on the left when a user searches "k cups" on Amazon.com. as a building block of e-commerce websites (e.g., Amazon.com) and business directory services (e.g., Yelp.com) for both customer-facing and internal applications, such as query understanding, item categorization [18], browsing, recommendation [9], and search [33]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Octet

Mao

Zhao

Kan

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Taxonomies have found wide applications in various domains, especially online for item categorization, browsing, and search. Despite the prevalent use of online catalog taxonomies, most of them in practice are maintained by humans, which is labor-intensive and difficult to scale. While taxonomy construction from scratch is considerably studied in the literature, how to effectively enrich existing incomplete taxonomies remains an open yet important research question. Taxonomy enrichment not only requires the robustness to deal with emerging terms but also the consistency between existing taxonomy structure and new term attachment. In this paper, we present a self-supervised end-to-end framework, Octet, for Online Catalog Taxonomy EnrichmenT. Octet leverages heterogeneous information unique to online catalog taxonomies such as user queries, items, and their relations to the taxonomy nodes while requiring no other supervision than the existing taxonomies. We propose to distantly train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure as well as the query-item-taxonomy interactions for term attachment. Extensive experiments in different online domains demonstrate the superiority of Octet over state-of-theart methods via both automatic and human evaluations. Notably, Octet enriches an online catalog taxonomy in production to 2 times larger in the open-world evaluation.

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Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation

Cited by 62 publications

References 43 publications

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

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