Mobile Query Recommendation via Tensor Function Learning
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 4091-4096.
Abstract
With the prevalence of mobile search nowadays, the benefits of mobile query recommendation are well recognized, which provide formulated queries sticking to users’ search intent. In this paper, we introduce the problem of query recommendation on mobile devices and model the user-location-query relations with a tensor representation. Unlike previous studies based on tensor decomposition, we study this problem via tensor function learning. That is, we learn the tensor function from the side information of users, locations and queries, and then predict users’ search intent. We develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the introduced problem. We empirically evaluate our approach based on the mobile query dataset from Bing search engine in the city of Beijing, China, and show that our method can outperform several state-of-the-art approaches.
@inproceedings{ijcai2015p573,
title = {Mobile Query Recommendation via Tensor Function Learning},
author = {Zhao, Zhou and Song, Ruihua and Xie, Xing and He, Xiaofei and Zhuang, Yueting},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {4091--4096},
year = {2015},
month = {7},
note = {Special Track on Machine Learning},
url = {https://ijcai.org/proceedings/2015/573},
}
TY - CPAPER TI - Mobile Query Recommendation via Tensor Function Learning AU - Zhao, Zhou AU - Song, Ruihua AU - Xie, Xing AU - He, Xiaofei AU - Zhuang, Yueting T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 4091 EP - 4096 UR - https://ijcai.org/proceedings/2015/573 ER -Copied
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