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- short-paperJuly 2023
AttriBERT - Session-based Product Attribute Recommendation with BERT
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 3421–3425https://doi.org/10.1145/3539618.3594714Finding the right product on e-commerce websites with millions of products is a daunting task for a large set of customers. On the search page, product attribute filters a.k.a. "refinements" emerge as a convenient navigational option for customers to ...
- tutorialJuly 2023
Complex Item Set Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 3444–3447https://doi.org/10.1145/3539618.3594248In this tutorial, we aim to shed light on the task of recommending a set of multiple items at once. In this scenario, historical interaction data between users and items could also be in the form of a sequence of interactions with sets of items. Complex ...
- tutorialJuly 2023
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 3440–3443https://doi.org/10.1145/3539618.3594247Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both an introduction to the core concepts of the field and an overview of ...
- tutorialJuly 2023
Causal Recommendation: Progresses and Future Directions
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 3432–3435https://doi.org/10.1145/3539618.3594245Data-driven recommender systems have demonstrated great success in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (ie correlation) from users' behaviors. However, they still suffer from ...
- short-paperJuly 2023
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
- Chunjing Gan,
- Binbin Hu,
- Bo Huang,
- Tianyu Zhao,
- Yingru Lin,
- Wenliang Zhong,
- Zhiqiang Zhang,
- Jun Zhou,
- Chuan Shi
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2516–2520https://doi.org/10.1145/3539618.3592088In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner. Consequently, we develop a novel Multi-...
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- short-paperJuly 2023
User-Dependent Learning to Debias for Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2491–2495https://doi.org/10.1145/3539618.3592083In recommender systems (RSs), inverse propensity score (IPS) has been a key technique to mitigate popularity bias by decreasing the contribution of popular items in modeling user-item interactions. However, conventional IPS treats all users equally, ...
- short-paperJuly 2023
Uncertainty-based Heterogeneous Privileged Knowledge Distillation for Recommendation System
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2471–2475https://doi.org/10.1145/3539618.3592079In industrial recommendation systems, both data sizes and computational resources vary across different scenarios. For scenarios with limited data, data sparsity can lead to a decrease in model performance. Heterogeneous knowledge distillation-based ...
- short-paperJuly 2023
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2466–2470https://doi.org/10.1145/3539618.3592078Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem, i.e., recommend for few-interaction items. Existing GNN-based ...
- short-paperJuly 2023
Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2461–2465https://doi.org/10.1145/3539618.3592077Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback. Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click ...
- short-paperJuly 2023
uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2456–2460https://doi.org/10.1145/3539618.3592076Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previous studies have utilized inverse propensity weighting (IPW) or causal ...
- short-paperJuly 2023
TAML: Time-Aware Meta Learning for Cold-Start Problem in News Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2415–2419https://doi.org/10.1145/3539618.3592068Meta-learning has become a widely used method for the user cold-start problem in recommendation systems, as it allows the model to learn from similar learning tasks and transfer the knowledge to new tasks. However, most existing meta-learning methods do ...
- short-paperJuly 2023
Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2384–2388https://doi.org/10.1145/3539618.3592062The advent of personalized news recommendation has given rise to increasingly complex recommender architectures. Most neural news recommenders rely on user click behavior and typically introduce dedicated user encoders that aggregate the content of ...
- short-paperJuly 2023
Sharpness-Aware Graph Collaborative Filtering
- Huiyuan Chen,
- Chin-Chia Michael Yeh,
- Yujie Fan,
- Yan Zheng,
- Junpeng Wang,
- Vivian Lai,
- Mahashweta Das,
- Hao Yang
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2369–2373https://doi.org/10.1145/3539618.3592059Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, recent studies show that GNNs tend to yield inferior performance when the distributions of training and test data are not aligned well. Moreover, ...
- short-paperJuly 2023
Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2354–2358https://doi.org/10.1145/3539618.3592056Going beyond accuracy in the evaluation of a recommender system is an aspect that is receiving more and more attention. Among the many perspectives that can be considered, the impact of presentation bias is of central importance. Under presentation bias, ...
- short-paperJuly 2023
Review-based Multi-intention Contrastive Learning for Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2339–2343https://doi.org/10.1145/3539618.3592053Real recommendation systems contain various features, which are often high-dimensional, sparse, and difficult to learn effectively. In addition to numerical features, user reviews contain rich semantic information including user preferences, which are ...
- short-paperJuly 2023
Quantifying and Leveraging User Fatigue for Interventions in Recommender Systems
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2293–2297https://doi.org/10.1145/3539618.3592044Predicting churn and designing intervention strategies are crucial for online platforms to maintain user engagement. We hypothesize that predicting churn, i.e. users leaving from the system without further return, is often a delayed act, and it might get ...
- short-paperJuly 2023
Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2272–2276https://doi.org/10.1145/3539618.3592040Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans would. To improve the accuracy of these models, some researchers have ...
- short-paperJuly 2023
Personalized Dynamic Recommender System for Investors
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2246–2250https://doi.org/10.1145/3539618.3592035With the development of online platforms, people can share and obtain opinions quickly. It also makes individuals' preferences change dynamically and rapidly because they may change their minds when getting convincing opinions from other users. Unlike ...
- short-paperJuly 2023
Optimizing Reciprocal Rank with Bayesian Average for improved Next Item Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2236–2240https://doi.org/10.1145/3539618.3592033Next item recommendation is a crucial task of session-based recommendation. However, the gap between the optimization objective (Binary Cross Entropy) and the ranking metric (Mean Reciprocal Rank) has not been well-explored, resulting in sub-optimal ...
- short-paperJuly 2023
Mining Interest Trends and Adaptively Assigning Sample Weight for Session-based Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 2174–2178https://doi.org/10.1145/3539618.3592021Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily ...