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U-rank: Utility-oriented Learning to Rank with Implicit Feedback

Dai, X; Hou, J; Liu, Q; Xi, Y; Tang, R; Zhang, W; He, X; ... Yu, Y; + view all (2020) U-rank: Utility-oriented Learning to Rank with Implicit Feedback. In: D'Aquin, M and Dietze, S and Hauff, C and Curry, E and Cudre Mauroux, P, (eds.) CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. (pp. pp. 2373-2380). Association for Computing Machinery (ACM): New York, NY, USA. Green open access

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Abstract

Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and revenue. However, we point out that existing methods based on probabilistic ranking principle do not necessarily achieve the highest utility. To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list. With a position-aware deep click-through rate prediction model, we address the attention bias considering both query-level and item-level features. Due to the item-specific attention bias modeling, the optimization for expected utility corresponds to a maximum weight matching on the item-position bipartite graph. We base the optimization of this objective in an efficient Lambdaloss framework, which is supported by both theoretical and empirical analysis. We conduct extensive experiments for both web search and recommender systems over three benchmark datasets and two proprietary datasets, where the performance gain of U-rank over state-of-the-arts is demonstrated. Moreover, our proposed U-rank has been deployed on a large-scale commercial recommender and a large improvement over the production baseline has been observed in an online A/B testing.

Type: Proceedings paper
Title: U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Event: 29th ACM International Conference on Information & Knowledge Management (CIKM '20)
ISBN-13: 9781450368599
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3340531.3412756
Publisher version: https://doi.org/10.1145/3340531.3412756
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10116267
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