Integrated Pricing of Overlapping Bundles via Optimizing Consumer Decisions

This paper examines a data-intensive optimization problem with an economically informed objective: given a set of bundles of items, we seek to price those bundles to maximize revenue. Going beyond existing work restricted to non-overlapping bundles, we address the more general scenario of overlapping bundles. This necessitates addressing two main challenges: determining the price of each bundle and calculating the revenue generated by a bundle at that price. Observing the mutual dependency between the two challenges, we propose the \textsc{Integrated Pricing Method}, a scalable approach that incorporates dependency-aware pricing. For the consumer demand problem, we motivate a principled utility maximization objective and solve it in terms of a weighted set packing formulation. In terms of bundle pricing, we introduce novel concepts such as competitive independence and dominance-based pruning. The effectiveness and efficiency of our approach are validated through experiments using real-world ratings-based datasets.

Installation

1. Clone the repository:

   git clone https://github.com/PreferredAI/integratedbundlepricing.git

2. Navigate to the project directory:

    cd integratedbundlepricing

3. Install the dependencies (requires Python 3.11.9)

    pip install -r requirements.txt

4. Navigate to the experiments directory:

    cd experiments

Usage

To obtain the results in Table II, run

python experiment_baseline_comparison.py

To obtain the results in Figure 5, run

python experiment_consumer_demand_problem.py

To obtain the results in Figure 6, run

python experiment_component_bmkc_v_random.py

To obtain the results in Figure 7, run

python experiment_component_bmkc_heuristic_scalability.py

To obtain the results in Figure 8, run

python experiment_component_comp_ind_v_random.py

To obtain the results in Figure 9, run

python experiment_component_lattice_v_graph.py

To obtain the results for pruning, run

python experiment_component_pruning.py

To obtain the results in Figure 10, run

python experiment_scalability_users.py 

and

python experiment_scalability_bundles.py