Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation

With the prevalence of mobile e-commerce nowadays, a new type of recommendation services, called intent recommendation, is widely used in many mobile e-commerce Apps, such as Taobao and Amazon. Different from traditional query recommendation and item recommendation, intent recommendation is to automatically recommend user intent according to user historical behaviors without any input when users open the App. Intent recommendation becomes very popular in the past two years, because of revealing user latent intents and avoiding tedious input in mobile phones. Existing methods used in industry usually need laboring feature engineering. Moreover, they only utilize attribute and statistic information of users and queries, and fail to take full advantage of rich interaction information in intent recommendation, which may result in limited performances. In this paper, we propose to model the complex objects and rich interactions in intent recommendation as a Heterogeneous Information Network. Furthermore, we present a novel M etapath-guided E mbedding method for I ntent Rec ommendation~(called MEIRec). In order to fully utilize rich structural information, we design a metapath-guided heterogeneous Graph Neural Network to learn the embeddings of objects in intent recommendation. In addition, in order to alleviate huge learning parameters in embeddings, we propose a uniform term embedding mechanism, in which embeddings of objects are made up with the same term embedding space. Offline experiments on real large-scale data show the superior performance of the proposed MEIRec, compared to representative methods.Moreover, the results of online experiments on Taobao e-commerce platform show that MEIRec not only gains a performance improvement of 1.54% on CTR metric, but also attracts up to 2.66% of new users to search queries.

[1]  Philip S. Yu,et al.  Heterogeneous Information Network Embedding for Recommendation , 2017, IEEE Transactions on Knowledge and Data Engineering.

[2]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[3]  Michael I. Jordan,et al.  On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.

[4]  Philip S. Yu,et al.  PathSim , 2011, Proc. VLDB Endow..

[5]  R. Real,et al.  AUC: a misleading measure of the performance of predictive distribution models , 2008 .

[6]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[7]  Philip S. Yu,et al.  A Survey of Heterogeneous Information Network Analysis , 2015, IEEE Transactions on Knowledge and Data Engineering.

[8]  Yanfang Ye,et al.  Heterogeneous Graph Attention Network , 2019, WWW.

[9]  Olfa Nasraoui,et al.  Mining search engine query logs for query recommendation , 2006, WWW '06.

[10]  Xing Xie,et al.  Mobile Query Recommendation via Tensor Function Learning , 2015, IJCAI.

[11]  Enhong Chen,et al.  Context-aware query suggestion by mining click-through and session data , 2008, KDD.

[12]  Philip S. Yu,et al.  Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks , 2015, CIKM.

[13]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[14]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[15]  Patrick Marcel,et al.  A survey of query recommendation techniques for data warehouse exploration , 2011, EDA.

[16]  Philip S. Yu,et al.  Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks , 2018, IJCAI.

[17]  Nitesh V. Chawla,et al.  metapath2vec: Scalable Representation Learning for Heterogeneous Networks , 2017, KDD.

[18]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[19]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[20]  Yuan Qi,et al.  Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism , 2019, AAAI.

[21]  Dik Lun Lee,et al.  Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks , 2017, KDD.

[22]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[23]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[24]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.