Using Stable Matching to Optimize the Balance between Accuracy and
Diversity in Recommendation
- URL: http://arxiv.org/abs/2006.03715v1
- Date: Fri, 5 Jun 2020 22:12:25 GMT
- Title: Using Stable Matching to Optimize the Balance between Accuracy and
Diversity in Recommendation
- Authors: Farzad Eskandanian, Bamshad Mobasher
- Abstract summary: Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains.
Attempts to increase aggregate diversity often result in lower recommendation accuracy for end users.
We propose a two-sided post-processing approach in which both user and item utilities are considered.
- Score: 3.0938904602244355
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Increasing aggregate diversity (or catalog coverage) is an important
system-level objective in many recommendation domains where it may be desirable
to mitigate the popularity bias and to improve the coverage of long-tail items
in recommendations given to users. This is especially important in
multistakeholder recommendation scenarios where it may be important to optimize
utilities not just for the end user, but also for other stakeholders such as
item sellers or producers who desire a fair representation of their items
across recommendation lists produced by the system. Unfortunately, attempts to
increase aggregate diversity often result in lower recommendation accuracy for
end users. Thus, addressing this problem requires an approach that can
effectively manage the trade-offs between accuracy and aggregate diversity. In
this work, we propose a two-sided post-processing approach in which both user
and item utilities are considered. Our goal is to maximize aggregate diversity
while minimizing loss in recommendation accuracy. Our solution is a
generalization of the Deferred Acceptance algorithm which was proposed as an
efficient algorithm to solve the well-known stable matching problem. We prove
that our algorithm results in a unique user-optimal stable match between items
and users. Using three recommendation datasets, we empirically demonstrate the
effectiveness of our approach in comparison to several baselines. In
particular, our results show that the proposed solution is quite effective in
increasing aggregate diversity and item-side utility while optimizing
recommendation accuracy for end users.
Related papers
- Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization [52.80408805368928]
We introduce a novel greedy-style subset selection algorithm for batch acquisition.
Our experiments on the red fluorescent proteins show that our proposed method achieves the baseline performance in 1.69x fewer queries.
arXiv Detail & Related papers (2024-06-21T05:57:08Z) - Robust portfolio optimization for recommender systems considering uncertainty of estimated statistics [2.928964540437144]
We propose a robust portfolio optimization model that copes with the uncertainty of estimated statistics based on the cardinality-based uncertainty sets.
Our method has the potential to improve the recommendation quality of various rating prediction algorithms.
arXiv Detail & Related papers (2024-06-09T15:42:54Z) - A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search [11.49911967350851]
This paper proposes a Preference-oriented Diversity Model Based on Mutual-information (PODM-MI)
PODM-MI consider both accuracy and diversity in the re-ranking process.
We have successfully deployed PODM-MI on an e-commerce search platform.
arXiv Detail & Related papers (2024-05-24T13:03:34Z) - Eliciting User Preferences for Personalized Multi-Objective Decision
Making through Comparative Feedback [76.7007545844273]
We propose a multi-objective decision making framework that accommodates different user preferences over objectives.
Our model consists of a Markov decision process with a vector-valued reward function, with each user having an unknown preference vector.
We suggest an algorithm that finds a nearly optimal policy for the user using a small number of comparison queries.
arXiv Detail & Related papers (2023-02-07T23:58:19Z) - Disentangled Representation for Diversified Recommendations [41.477162048806434]
Accuracy and diversity have long been considered to be two conflicting goals for recommendations.
We propose a general diversification framework agnostic to the choice of recommendation algorithms.
Our solution disentangles the learnt user representation in the recommendation module into category-independent and category-dependent components.
arXiv Detail & Related papers (2023-01-13T11:47:10Z) - Diversely Regularized Matrix Factorization for Accurate and Aggregately
Diversified Recommendation [15.483426620593013]
DivMF (Diversely Regularized Matrix Factorization) is a novel matrix factorization method for aggregately diversified recommendation.
We show that DivMF achieves the state-of-the-art performance in aggregately diversified recommendation.
arXiv Detail & Related papers (2022-10-19T08:49:39Z) - Recommendation Systems with Distribution-Free Reliability Guarantees [83.80644194980042]
We show how to return a set of items rigorously guaranteed to contain mostly good items.
Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate.
We evaluate our methods on the Yahoo! Learning to Rank and MSMarco datasets.
arXiv Detail & Related papers (2022-07-04T17:49:25Z) - Introducing a Framework and a Decision Protocol to Calibrate Recommender
Systems [0.0]
This paper proposes an approach to create recommendation lists with a calibrated balance of genres.
The main claim is that calibration can contribute positively to generate fairer recommendations.
We propose a conceptual framework and a decision protocol to generate more than one thousand combinations of calibrated systems.
arXiv Detail & Related papers (2022-04-07T19:30:55Z) - Optimizer Amalgamation [124.33523126363728]
We are motivated to study a new problem named Amalgamation: how can we best combine a pool of "teacher" amalgamations into a single "student" that can have stronger problem-specific performance?
First, we define three differentiable mechanisms to amalgamate a pool of analyticals by gradient descent.
In order to reduce variance of the process, we also explore methods to stabilize the process by perturbing the target.
arXiv Detail & Related papers (2022-03-12T16:07:57Z) - Choosing the Best of Both Worlds: Diverse and Novel Recommendations
through Multi-Objective Reinforcement Learning [68.45370492516531]
We introduce Scalarized Multi-Objective Reinforcement Learning (SMORL) for the Recommender Systems (RS) setting.
SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations.
Our experimental results on two real-world datasets reveal a substantial increase in aggregate diversity, a moderate increase in accuracy, reduced repetitiveness of recommendations, and demonstrate the importance of reinforcing diversity and novelty as complementary objectives.
arXiv Detail & Related papers (2021-10-28T13:22:45Z) - SetRank: A Setwise Bayesian Approach for Collaborative Ranking from
Implicit Feedback [50.13745601531148]
We propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to accommodate the characteristics of implicit feedback in recommender system.
Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons.
We also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to $sqrtM/N$.
arXiv Detail & Related papers (2020-02-23T06:40:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.