Simultaneous Relevance and Diversity: A New Recommendation Inference Approach

• URL: http://arxiv.org/abs/2009.12969v1
• Date: Sun, 27 Sep 2020 22:20:12 GMT
• Title: Simultaneous Relevance and Diversity: A New Recommendation Inference Approach
• Authors: Yifang Liu, Zhentao Xu, Qiyuan An, Yang Yi, Yanzhi Wang, Trevor Hastie
• Abstract summary: We propose a new approach, which extends the general collaborative filtering (CF) by introducing a new way of CF inference, negative-to-positive. Our approach is applicable to a wide range of recommendation scenarios/use-cases at various sophistication levels. Our analysis and experiments on public datasets and real-world production data show that our approach outperforms existing methods on relevance and diversity simultaneously.
• Score: 81.44167398308979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relevance and diversity are both important to the success of recommender systems, as they help users to discover from a large pool of items a compact set of candidates that are not only interesting but exploratory as well. The challenge is that relevance and diversity usually act as two competing objectives in conventional recommender systems, which necessities the classic trade-off between exploitation and exploration. Traditionally, higher diversity often means sacrifice on relevance and vice versa. We propose a new approach, heterogeneous inference, which extends the general collaborative filtering (CF) by introducing a new way of CF inference, negative-to-positive. Heterogeneous inference achieves divergent relevance, where relevance and diversity support each other as two collaborating objectives in one recommendation model, and where recommendation diversity is an inherent outcome of the relevance inference process. Benefiting from its succinctness and flexibility, our approach is applicable to a wide range of recommendation scenarios/use-cases at various sophistication levels. Our analysis and experiments on public datasets and real-world production data show that our approach outperforms existing methods on relevance and diversity simultaneously.

Related papers

• Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations [15.143224593682012]
  We propose a novel recommendation strategy that combines relevance and diversity by a copula function. We use diversity as a surrogate of the amount of knowledge obtained by the user while interacting with the system. Our strategy outperforms several state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-07T13:48:24Z)
• BiVRec: Bidirectional View-based Multimodal Sequential Recommendation [55.87443627659778]
  We propose an innovative framework, BivRec, that jointly trains the recommendation tasks in both ID and multimodal views. BivRec achieves state-of-the-art performance on five datasets and showcases various practical advantages.
  arXiv  Detail & Related papers  (2024-02-27T09:10:41Z)
• Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning [60.058083574671834]
  This paper presents a novel FCCL+, federated correlation and similarity learning with non-target distillation. For heterogeneous issue, we leverage irrelevant unlabeled public data for communication. For catastrophic forgetting in local updating stage, FCCL+ introduces Federated Non Target Distillation.
  arXiv  Detail & Related papers  (2023-09-28T09:32:27Z)
• Dynamic fairness-aware recommendation through multi-agent social choice [10.556124653827647]
  We argue that fairness in real-world application settings in general, and especially in the context of personalized recommendation, is much more complex and multi-faceted. We propose a model to formalize multistakeholder fairness in recommender systems as a two stage social choice problem.
  arXiv  Detail & Related papers  (2023-03-02T05:06:17Z)
• Performative Recommendation: Diversifying Content via Strategic Incentives [13.452510519858995]
  We show how learning can incentivize strategic content creators to create diverse content. Our approach relies on a novel form of regularization that anticipates strategic changes to content.
  arXiv  Detail & Related papers  (2023-02-08T21:02:28Z)
• Variational Distillation for Multi-View Learning [104.17551354374821]
  We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
  arXiv  Detail & Related papers  (2022-06-20T03:09:46Z)
• Diversity Preference-Aware Link Recommendation for Online Social Networks [0.2580765958706854]
  Existing link recommendation methods tend to recommend similar friends to a user but overlook the user's diversity preference. We propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then analyze key properties of the new link recommendation problem and develop a novel link recommendation method to solve the problem.
  arXiv  Detail & Related papers  (2022-05-21T22:59:00Z)
• 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)
• Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations [85.6250759280292]
  Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. Existing cross-domain models typically require large number of overlap users, which can be difficult to obtain in some applications. We propose a novel cross-domain recommendation model based on dual learning that transfers information between two related domains in an iterative manner.
  arXiv  Detail & Related papers  (2021-04-17T09:18:59Z)

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.