Towards Explainable Scientific Venue Recommendations

• URL: http://arxiv.org/abs/2109.11343v1
• Date: Tue, 21 Sep 2021 10:25:26 GMT
• Title: Towards Explainable Scientific Venue Recommendations
• Authors: Bastian Sch\"afermeier and Gerd Stumme and Tom Hanika
• Abstract summary: We propose an unsophisticated method that advances the state-of-the-art in this area. First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models. Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.
• Score: 0.09668407688201358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models; Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.

Related papers

• Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method [60.364834418531366]
  We propose five new evaluation metrics that comprehensively and accurately assess the performance of RRS. We formulate the RRS from a causal perspective, formulating recommendations as bilateral interventions. We introduce a reranking strategy to maximize matching outcomes, as measured by the proposed metrics.
  arXiv  Detail & Related papers  (2024-08-19T07:21:02Z)
• Improving Subject-Driven Image Synthesis with Subject-Agnostic Guidance [62.15866177242207]
  We show that through constructing a subject-agnostic condition, one could obtain outputs consistent with both the given subject and input text prompts. Our approach is conceptually simple and requires only minimal code modifications, but leads to substantial quality improvements.
  arXiv  Detail & Related papers  (2024-05-02T15:03:41Z)
• Embedding in Recommender Systems: A Survey [67.67966158305603]
  A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors. Applying embedding techniques captures complex entity relationships and has spurred substantial research. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
  arXiv  Detail & Related papers  (2023-10-28T06:31:06Z)
• Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender Systems [7.994912533454301]
  We argue for the importance of offering novel and diverse research paper recommendations to scientists. This approach aims to reduce siloed reading, break down filter bubbles, and promote interdisciplinary research.
  arXiv  Detail & Related papers  (2023-09-26T14:56:56Z)
• Impression-Aware Recommender Systems [57.38537491535016]
  Novel data sources bring new opportunities to improve the quality of recommender systems. Researchers may use impressions to refine user preferences and overcome the current limitations in recommender systems research. We present a systematic literature review on recommender systems using impressions.
  arXiv  Detail & Related papers  (2023-08-15T16:16:02Z)
• Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation [0.0]
  We are the first to apply the Fisher-Merging method to Sequential Recommendation, addressing and resolving practical challenges associated with it. We demonstrate the effectiveness of our proposed methods, highlighting their potential to advance the state-of-the-art in sequential learning and recommendation systems.
  arXiv  Detail & Related papers  (2023-07-05T05:58:56Z)
• Recommendation Fairness: From Static to Dynamic [12.080824433982993]
  We discuss how fairness could be baked into reinforcement learning techniques for recommendation. We argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization.
  arXiv  Detail & Related papers  (2021-09-05T21:38:05Z)
• A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions [27.894300356732696]
  We propose a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner.
  arXiv  Detail & Related papers  (2021-08-07T03:26:16Z)
• Self-Supervised Reinforcement Learning for Recommender Systems [77.38665506495553]
  We propose self-supervised reinforcement learning for sequential recommendation tasks. Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL. Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC)
  arXiv  Detail & Related papers  (2020-06-10T11:18:57Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)
• State-of-Art-Reviewing: A Radical Proposal to Improve Scientific Publication [19.10668029301668]
  State-Of-the-Art Review (SOAR) is a neoteric reviewing pipeline that serves as a 'plug-and-play' replacement for peer review. At the heart of our approach is an interpretation of the review process as a multi-objective, massively distributed and extremely-high-latency optimisation.
  arXiv  Detail & Related papers  (2020-03-31T17:58:36Z)

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.