2012 MultipleObjectiveOptimizationin

(Rodriguez et al., 2012) ⇒ Mario Rodriguez, Christian Posse, and Ethan Zhang. (2012). “Multiple Objective Optimization in Recommender Systems.” In: Proceedings of the sixth ACM conference on Recommender systems. ISBN:978-1-4503-1270-7 doi:10.1145/2365952.2365961

Subject Headings: Multiple Objective Optimization.

Notes

Cited By

Quotes

Abstract

We address the problem of optimizing recommender systems for multiple relevance objectives that are not necessarily aligned. Specifically, given a recommender system that optimizes for one aspect of relevance, semantic matching (as defined by any notion of similarity between source and target of recommendation; usually trained on CTR), we want to enhance the system with additional relevance signals that will increase the utility of the recommender system, but that may simultaneously sacrifice the quality of the semantic match. The issue is that semantic matching is only one relevance aspect of the utility function that drives the recommender system, albeit a significant aspect. In talent recommendation systems, job posters want candidates who are a good match to the job posted, but also prefer those candidates to be open to new opportunities. Recommender systems that recommend discussion groups must ensure that the groups are relevant to the users' interests, but also need to favor active groups over inactive ones. We refer to these additional relevance signals (job-seeking intent and group activity) as extraneous features, and they account for aspects of the utility function that are not captured by the semantic match (i.e. post-CTR down-stream utilities that reflect engagement: time spent reading, sharing, commenting, etc). We want to include these extraneous features into the recommendations, but we want to do so while satisfying the following requirements: 1) we do not want to drastically sacrifice the quality of the semantic match, and 2) we want to quantify exactly how the semantic match would be affected as we control the different aspects of the utility function. In this paper, we present an approach that satisfies these requirements.

We frame our approach as a general constrained optimization problem and suggest ways in which it can be solved efficiently by drawing from recent research on optimizing non-smooth rank metrics for information retrieval. Our approach features the following characteristics: 1) it is model and feature agnostic, 2) it does not require additional labeled training data to be collected, and 3) it can be easily incorporated into an existing model as an additional stage in the computation pipeline. We validate our approach in a revenue-generating recommender system that ranks billions of candidate recommendations on a daily basis and show that a significant improvement in the utility of the recommender system can be achieved with an acceptable and predictable degradation in the semantic match quality of the recommendations.

References

1. Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Xuanhui Wang, Click Shaping to Optimize Multiple Objectives, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 21-24, 2011, San Diego, California, USA doi:10.1145/2020408.2020435
2. S.-H. Cha. Comprehensive Survey on Distance/similarity Measures Between Probability Density Functions. International Journal of Mathematical Models and Methods in Applied Sciences, 2007.
3. Olivier Chapelle, Mingrui Wu, Gradient Descent Optimization of Smoothed Information Retrieval Metrics, Information Retrieval, v.13 n.3, p.216-235, June 2010 doi:10.1007/s10791-009-9110-3
4. E. C. Fieller. Some Problems in Interval Estimation. Journal of the Royal Statistical Society, 1954.
5. Tamas Jambor, Jun Wang, Optimizing Multiple Objectives in Collaborative Filtering, Proceedings of the Fourth ACM Conference on Recommender Systems, September 26-30, 2010, Barcelona, Spain doi:10.1145/1864708.1864723
6. Yaochu Jin, B. Sendhoff, Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, v.38 n.3, p.397-415, May 2008 doi:10.1109/TSMCC.2008.919172
7. Changsung Kang, Xuanhui Wang, Yi Chang, Belle Tseng, Learning to Rank with Multi-aspect Relevance for Vertical Search, Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, February 08-12, 2012, Seattle, Washington, USA doi:10.1145/2124295.2124350
8. Krysta M. Svore, Maksims N. Volkovs, Christopher J.C. Burges, Learning to Rank with Multiple Objective Functions, Proceedings of the 20th International Conference on World Wide Web, March 28-April 01, 2011, Hyderabad, India doi:10.1145/1963405.1963459
9. Michael Taylor, John Guiver, Stephen Robertson, Tom Minka, SoftRank: Optimizing Non-smooth Rank Metrics, Proceedings of the 2008 International Conference on Web Search and Data Mining, February 11-12, 2008, Palo Alto, California, USA doi:10.1145/1341531.1341544

}};

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MultipleObjectiveOptimizationin	Christian Posse Mario Rodriguez Ethan Zhang			Multiple Objective Optimization in Recommender Systems				10.1145/2365952.2365961		2012