Buyer to Seller Recommendation under Constraints
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by
Cheng Chen, Lan Zheng, Venkatesh Srinivasan, Alex Thomo, Kui Wu,
Anthony Sukow
2014
Abstract
The majority of recommender systems are designed to recommend items (such as
movies and products) to users. We focus on the problem of recommending buyers
to sellers which comes with new challenges: (1) constraints on the number of
recommendations buyers are part of before they become overwhelmed, (2)
constraints on the number of recommendations sellers receive within their
budget, and (3) constraints on the set of buyers that sellers want to receive
(e.g., no more than two people from the same household). We propose the
following critical problems of recommending buyers to sellers: Constrained
Recommendation (C-REC) capturing the first two challenges, and Conflict-Aware
Constrained Recommendation (CAC-REC) capturing all three challenges at the same
time. We show that C-REC can be modeled using linear programming and can be
efficiently solved using modern solvers. On the other hand, we show that
CAC-REC is NP-hard. We propose two approximate algorithms to solve CAC-REC and
show that they achieve close to optimal solutions via comprehensive experiments
using real-world datasets.
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