7 research outputs found
Diverse Weighted Bipartite b-Matching
Bipartite matching, where agents on one side of a market are matched to
agents or items on the other, is a classical problem in computer science and
economics, with widespread application in healthcare, education, advertising,
and general resource allocation. A practitioner's goal is typically to maximize
a matching market's economic efficiency, possibly subject to some fairness
requirements that promote equal access to resources. A natural balancing act
exists between fairness and efficiency in matching markets, and has been the
subject of much research.
In this paper, we study a complementary goal---balancing diversity and
efficiency---in a generalization of bipartite matching where agents on one side
of the market can be matched to sets of agents on the other. Adapting a
classical definition of the diversity of a set, we propose a quadratic
programming-based approach to solving a supermodular minimization problem that
balances diversity and total weight of the solution. We also provide a scalable
greedy algorithm with theoretical performance bounds. We then define the price
of diversity, a measure of the efficiency loss due to enforcing diversity, and
give a worst-case theoretical bound. Finally, we demonstrate the efficacy of
our methods on three real-world datasets, and show that the price of diversity
is not bad in practice
Improving End-to-End Sequential Recommendations with Intent-aware Diversification
Sequential Recommendation (SRs) that capture users' dynamic intents by
modeling user sequential behaviors can recommend closely accurate products to
users. Previous work on SRs is mostly focused on optimizing the recommendation
accuracy, often ignoring the recommendation diversity, even though it is an
important criterion for evaluating the recommendation performance. Most
existing methods for improving the diversity of recommendations are not ideally
applicable for SRs because they assume that user intents are static and rely on
post-processing the list of recommendations to promote diversity. We consider
both recommendation accuracy and diversity for SRs by proposing an end-to-end
neural model, called Intent-aware Diversified Sequential Recommendation (IDSR).
Specifically, we introduce an Implicit Intent Mining module (IIM) into SRs to
capture different user intents reflected in user behavior sequences. Then, we
design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning
of the IIM module and force the model to take recommendation diversity into
consideration during training. Extensive experiments on two benchmark datasets
show that IDSR significantly outperforms state-of-the-art methods in terms of
recommendation diversity while yielding comparable or superior recommendation
accuracy