Deep Learning Based Sentiment Aware Ranking for E-commerce Product Search
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-90639-9_7
Reference26 articles.
1. Ai, Q., Hill, D.N., Vishwanathan, S.V.N., Croft, W.B.: A zero attention model for personalized product search. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China, CIKM 2019, pp. 379–388. Association for Computing Machinery, November 2019. https://doi.org/10.1145/3357384.3357980
2. Ai, Q., Zhang, Y., Bi, K., Chen, X., Croft, W.B.: Learning a hierarchical embedding model for personalized product search. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, SIGIR 2017, pp. 645–654. Association for Computing Machinery, August 2017. https://doi.org/10.1145/3077136.3080813
3. Bell, A., Senthil Kumar, P., Miranda, D.: The title says it all: a title term weighting strategy for eCommerce ranking. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, CIKM 2018, pp. 2233–2241. Association for Computing Machinery, October 2018. https://doi.org/10.1145/3269206.3272033
4. Bi, K., Ai, Q., Zhang, Y., Croft, W.B.: Conversational product search based on negative feedback. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China, CIKM 2019, pp. 359–368. Association for Computing Machinery, November 2019. https://doi.org/10.1145/3357384.3357939
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