The NodeHopper: Enabling Low Latency Ranking with Constraints via a Fast Dual Solver
| Autor: | Ivan Lobov, Anton Zhernov, Krishnamurthy Dvijotham, Natarajan Chandrashekar, Michelle Gong, Dan Andrei Calian, Timothy A. Mann |
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| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Optimization problem Linear programming Computer science 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Latency (engineering) Recommender system Solver Ranking (information retrieval) |
| Zdroj: | KDD |
| Popis: | Modern recommender systems need to deal with multiple objectives like balancing user engagement with recommending diverse and fresh content. An appealing way to optimally trade these off is by imposing constraints on the ranking according to which items are presented to a user. This results in a constrained ranking optimization problem that can be solved as a linear program (LP). However, off-the-shelf LP solvers are unable to meet the severe latency constraints in systems that serve live traffic. To address this challenge, we exploit the structure of the dual optimization problem to develop a fast solver. We analyze theoretical properties of our solver and show experimentally that it is able to solve constrained ranking problems on synthetic and real-world recommendation datasets an order of magnitude faster than off-the-shelf solvers, thereby enabling their deployment under severe latency constraints. |
| Databáze: | OpenAIRE |
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