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Context-Aware Deep Model for Entity Recommendation System in Search Engine at Alibaba
Qianghuai Jia, Ningyu Zhang, Nengwei Hua
Alibaba Group & Hangzhou. China
Abstract: Entity recommendation, providing search users with an improved experience via assisting them in finding related entities for a given query, has become an indispensable feature of today’s search engines. Existing studies typically only consider the queries with explicit entities. They usually fail to handle complex queries that without entities, such as “what food is good for cold weather”, because their models could not infer the underlying meaning of the input text. In this work, we believe that contexts convey valuable evidence that could facilitate the semantic modeling of queries, and take them into consideration for entity recommendation. In order to better model the semantics of queries and entities, we learn the representation of queries and entities jointly with attentive deep neural networks. We evaluate our approach using largescale, realworld search logs from a widely used commercial Chinese search engine. Our system has been deployed in ShenMa Search Engine 1 and you can fetch it in UC Browser of Alibaba. Results from online A/B test suggest that the impression efficiency of click-through rate increased by 5.1% and page view increased by 5.5%.
Keywords: Entity Recommendation, Deep Neural Networks, Query Understanding, Knowledge Graph, Cognitive Concept Graph Context-Aware Deep Model for Entity Recommendation System in Search Engine at Alibaba
DOI:https://doi.org/10.6025/jmpt/2020/11/1/23-35
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References:

[1] Blanc, Guy., Rendle, Steffen. (2017). Adaptive sampled softmax with kernel based sampling. arXiv preprint arXiv:1712.00527.
[2] Blanco, Roi., Barla Cambazoglu, Berkant., Mika, Peter., Torzec, Nicolas. (2013). Entity recommendations in web search. In: International SemanticWeb Conference. Springer, 33–48.
[3] Covington, Paul., Adams, Jay., Sargin, Emre. (2016). Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems. ACM, 191–198.
[4] Devlin, Jacob., Chang, Ming-Wei., Lee, Kenton., Toutanova, Kristina. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
[5] Fernández-Tobías, Ignacio., Blanco, Roi. (2016). Memory-based recommendations of entities for web search users. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 35–44.
[6] Grover, Aditya., Leskovec, Jure. (2016). node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 855–864.
[7] Huang, Jizhou., Zhang, Wei., Sun, Yaming., Wang, Haifeng., Liu, Ting. (2018). Improving Entity Recommendation with Search Log and Multi-Task Learning. In IJCAI. 4107–4114.
[8] Huang, Jizhou., Zhao, Shiqi., Ding, Shiqiang., Wu, Haiyang., Sun, Mingming., Wang, Haifeng. (2016). Generating Recommendation Evidence using Translation Model. In IJCAI. 2810–2816.
[9] Jayaram, Nandish., Gupta, Mahesh., Khan, Arijit., Li, Chengkai., Yan, Xifeng., Elmasri, Ramez. (2014). GQBE: Querying knowledge graphs by example entity tuples. In: 2014 IEEE 30th International Conference on Data Engineering. IEEE, 1250–1253.
[10] Joulin, Armand., Grave, Edouard., Bojanowski, Piotr., Douze, Matthijs., Jégou, Hérve., Mikolov, Tomas. (2016). Fasttext. zip: Compressing text classification models. arXiv preprint arXiv:1612.03651.
[11] Metzger, Steffen., Schenkel, Ralf., Sydow, Marcin. (2013). Qbees: query by entity examples. In: Proceedings of the 22nd ACM international conference on Information & Knowledge Management. ACM, 1829–1832.
[12] Mikolov, Tomas., Sutskever, Ilya., Chen, Kai., Greg S Corrado, Dean, Jeff. (2013). Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems. 3111–3119.
[13] Mottin, Davide., Lissandrini, Matteo., Velegrakis, Yannis., Palpanas, Themis. (2014). Exemplar queries: Give me an example of what you need. In: Proceedings of the VLDB Endowment 7, 5 (2014), 365–376.
[14] Wang, Hongwei., Zhang, Fuzheng., Xie, Xing., Guo, Minyi. (2018). DKN: Deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 World WideWeb Conference. International World Wide Web Conferences Steering Committee, 1835–1844.
[15] Yang, Zhilin., Dai, Zihang., Yang, Yiming., Carbonell, Jaime., Salakhutdinov, Ruslan., Quoc V Le. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. arXiv preprint arXiv:1906.08237.
[16] Zhang, Ningyu., Deng, Shumin., Sun, Zhanlin., Chen, Xi., Zhang, Wei., Chen, Huajun. (2018). Attention-based capsule networks with dynamic routing for relation extraction. arXiv preprint arXiv:1812.11321.
[17] Zhang, Ningyu., Deng, Shumin., Sun, Zhanlin., Wang, Guanying., Chen, Xi., Zhang, Wei., Chen, Huajun. (2019). Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks. arXiv preprint arXiv:1903.01306.
[18] Zhu, Han., Li, Xiang., Zhang, Pengye., Li, Guozheng., He, Jie., Li, Han., Gai. (2018). Learning Tree-based Deep Model for Recommender Systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1079–1088.


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