TY - GEN
T1 - Task-based recommendation on a web-scale
AU - Zhang, Yongfeng
AU - Zhang, Min
AU - Liu, Yiqun
AU - Tat-Seng, Chua
AU - Zhang, Yi
AU - Ma, Shaoping
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - The Web today has gone far beyond a tool for simply posting and retrieving information, but a universal platform to accomplish various kinds of tasks in daily life. However, research and application of personalized recommendation are still mostly restricted to intra-site vertical recom-menders, such as video recommendation in YouTube, or product recommendation in Amazon. Usually, they treat users' historical behaviors as discrete records, extract collaborative relations therein, and provide intra-site homogeneous recommendations, without specific consideration of the underlying tasks that inherently drive users' browsing actions. In this paper, we propose task-based recommendation to offer cross-site heterogenous item recommendations on a Web-scale, which better meet users' potential demands in a task, e.g., one may turn to Amazon for the dress worn by an actress after watching a video on YouTube, or may turn to car rental websites to rent a car after booking a hotel online. We believe that task-based recommendation would be one of the key components to the next generation of universal web-scale recommendation engines. Technically, we formalize tasks as demand sequences embedded in user browsing sessions, and extract frequent demand sequences from large scale browser logs recorded by a well known commercial web browser. Based on these demand sequences, we predict the upcoming demand of a user given the current browsing session, and further provide personalized heterogeneous recommendations that meet the predicted demands. Extensive experiments on cross-site heterogenous recommendation with real-world browsing data verified the effectiveness of our framework.
AB - The Web today has gone far beyond a tool for simply posting and retrieving information, but a universal platform to accomplish various kinds of tasks in daily life. However, research and application of personalized recommendation are still mostly restricted to intra-site vertical recom-menders, such as video recommendation in YouTube, or product recommendation in Amazon. Usually, they treat users' historical behaviors as discrete records, extract collaborative relations therein, and provide intra-site homogeneous recommendations, without specific consideration of the underlying tasks that inherently drive users' browsing actions. In this paper, we propose task-based recommendation to offer cross-site heterogenous item recommendations on a Web-scale, which better meet users' potential demands in a task, e.g., one may turn to Amazon for the dress worn by an actress after watching a video on YouTube, or may turn to car rental websites to rent a car after booking a hotel online. We believe that task-based recommendation would be one of the key components to the next generation of universal web-scale recommendation engines. Technically, we formalize tasks as demand sequences embedded in user browsing sessions, and extract frequent demand sequences from large scale browser logs recorded by a well known commercial web browser. Based on these demand sequences, we predict the upcoming demand of a user given the current browsing session, and further provide personalized heterogeneous recommendations that meet the predicted demands. Extensive experiments on cross-site heterogenous recommendation with real-world browsing data verified the effectiveness of our framework.
KW - Browser log analysis
KW - Collaborative filtering
KW - Demand prediction
KW - Task mining
KW - Task-based recommendation
UR - http://www.scopus.com/inward/record.url?scp=84963730217&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963730217&partnerID=8YFLogxK
U2 - 10.1109/BigData.2015.7363829
DO - 10.1109/BigData.2015.7363829
M3 - Conference contribution
AN - SCOPUS:84963730217
T3 - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
SP - 827
EP - 836
BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
A2 - Luo, Feng
A2 - Ogan, Kemafor
A2 - Zaki, Mohammed J.
A2 - Haas, Laura
A2 - Ooi, Beng Chin
A2 - Kumar, Vipin
A2 - Rachuri, Sudarsan
A2 - Pyne, Saumyadipta
A2 - Ho, Howard
A2 - Hu, Xiaohua
A2 - Yu, Shipeng
A2 - Hsiao, Morris Hui-I
A2 - Li, Jian
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Y2 - 29 October 2015 through 1 November 2015
ER -