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Show Me the Money:
Dynamic Recommendations for Revenue Maximization
Wei Lu Shanshan Chen Keqian Li LaksV.S. Lakshmanan
Department of Computer Science
University of British Columbia,Vancouver, Canada
©Wei Lu welu@cs.ubc.ca
User-centric
 The focus of most existing work
 Predict user preferences and unknown ratings
 Typical objective: minimize prediction errors (e.g.,
RMSE minimization in predicted ratings)
 Recommendations are generated individually
Business-centric
 The focus of this work
 Recommendations should not only be relevant, but
also driven by real business objectives
 Important examples: revenue and profit
 Recommendations are generated holistically
Paradigm Shift for Recommender Systems
A discrete time horizon 𝑡 = 1, … , 𝑇 Each user is recommended up to 𝑘 items in
each time step (partition matroid constraint)
Each item 𝑖 has a price 𝑝(𝑖, 𝑡) at time step 𝑡,
a capacity constraint, and a class label
Connecting Recommendations and Revenue
Revenue Maximization: Problem Definition & Solutions
Selected Experimental Results
Maximize expected revenue yielded by
recommendations (user-item-time triples)
 NP-hard
 𝑅(𝑆) is submodular & non-monotone
 Local Search approximation: 1/(4 + 𝜖) factor (Lee et al., STOC 2009)
 Global Greedy: Select recommendation triples greedily,
optimized by two-level heap structure for better efficiency
 Sequential & Randomized Local Greedy: greedy heuristics
operated in individual time steps
Discussions & Future Directions
 Random price models (distributions of prices
are known): use Taylor Approximation
 What if prices are completely unknown?
(pricing needs to be included in solution)
 Does NP-hardness of revenue maximization
remain if every item belongs to its own class?
 What are other worthwhile business
objectives to optimize
 Base adoption probability: 𝑞 𝐴𝑙𝑖𝑐𝑒, 𝑡𝑜𝑦𝐷𝑜𝑔, 𝑡1 =
0.2, 𝑞 𝐴𝑙𝑖𝑐𝑒, 𝑡𝑜𝑦𝐷𝑜𝑔, 𝑡2 = 0.3 (price goes down)
 Saturation: overly repeated impressions may lead to
boredom & aversion
 Competition: users only adopt at most one item
from each class
Dynamic adoption probability
Assuming: toyCat is recommended to Alice in t1 and belongs to the same
class as toyDog

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VLDB 2015 poster: Revenue Maximization in Recommender Systems

  • 1. Show Me the Money: Dynamic Recommendations for Revenue Maximization Wei Lu Shanshan Chen Keqian Li LaksV.S. Lakshmanan Department of Computer Science University of British Columbia,Vancouver, Canada ©Wei Lu welu@cs.ubc.ca User-centric  The focus of most existing work  Predict user preferences and unknown ratings  Typical objective: minimize prediction errors (e.g., RMSE minimization in predicted ratings)  Recommendations are generated individually Business-centric  The focus of this work  Recommendations should not only be relevant, but also driven by real business objectives  Important examples: revenue and profit  Recommendations are generated holistically Paradigm Shift for Recommender Systems A discrete time horizon 𝑡 = 1, … , 𝑇 Each user is recommended up to 𝑘 items in each time step (partition matroid constraint) Each item 𝑖 has a price 𝑝(𝑖, 𝑡) at time step 𝑡, a capacity constraint, and a class label Connecting Recommendations and Revenue Revenue Maximization: Problem Definition & Solutions Selected Experimental Results Maximize expected revenue yielded by recommendations (user-item-time triples)  NP-hard  𝑅(𝑆) is submodular & non-monotone  Local Search approximation: 1/(4 + 𝜖) factor (Lee et al., STOC 2009)  Global Greedy: Select recommendation triples greedily, optimized by two-level heap structure for better efficiency  Sequential & Randomized Local Greedy: greedy heuristics operated in individual time steps Discussions & Future Directions  Random price models (distributions of prices are known): use Taylor Approximation  What if prices are completely unknown? (pricing needs to be included in solution)  Does NP-hardness of revenue maximization remain if every item belongs to its own class?  What are other worthwhile business objectives to optimize  Base adoption probability: 𝑞 𝐴𝑙𝑖𝑐𝑒, 𝑡𝑜𝑦𝐷𝑜𝑔, 𝑡1 = 0.2, 𝑞 𝐴𝑙𝑖𝑐𝑒, 𝑡𝑜𝑦𝐷𝑜𝑔, 𝑡2 = 0.3 (price goes down)  Saturation: overly repeated impressions may lead to boredom & aversion  Competition: users only adopt at most one item from each class Dynamic adoption probability Assuming: toyCat is recommended to Alice in t1 and belongs to the same class as toyDog