Session 9: Collaborative filtering 2

Date: Thursday September 21, 2:00 PM – 3:20 PM (GMT+8)
Room: Hall 406CX
Session Chair: Markus Schedl
Parallel with: Session 10: Reinforcement Learning

• RESRethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
  by Yueqi Xie (HKUST), Jingqi Gao (Upstage), Peilin Zhou (HKUST (gz)), Qichen Ye (Peking University), Yining Hua (Massachusetts Institute of Technology), Jae Boum Kim (Hong Kong University of Science and Technology), Fangzhao Wu (MSRA) and Sunghun Kim (Hong Kong University of Science and Technology).

  Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representations. First, the current training objective (i.e., uniformly sampled softmax) fails to effectively train discriminative representations in a multi-interest learning scenario due to the severe increase in easy negative samples. Second, a routing collapse problem is observed where each learned interest may collapse to express information only from a single item, resulting in information loss. To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code is available at https://anonymous.4open.science/r/ReMIRec-B64C/.

  Full text in ACM Digital Library

• RESTrending Now: Modeling Trend Recommendations
  by Hao Ding (AWS AI Labs), Branislav Kveton (AWS AI Labs), Yifei Ma (AWS AI Labs), Youngsuk Park (AWS AI Labs), Venkataramana Kini (AWS AI Labs), Yupeng Gu (AWS AI Labs), Ravi Divvela (AWS AI Labs), Fei Wang (AWS AI Labs), Anoop Deoras (AWS AI Labs) and Hao Wang (AWS AI Labs).

  Modern recommender systems usually include separate recommendation carousels such as ‘trending now’ to list trending items and further boost their popularity, thereby attracting active users. Though widely useful, such ‘trending now‘ carousels typically generate item lists based on simple heuristics, e.g., the number of interactions within a time interval, and therefore still leave much room for improvement. This paper aims to systematically study this under-explored but important problem from the new perspective of time series forecasting. We first provide a set of rigorous definitions related to item trendiness with associated evaluation protocols, and then propose a deep latent variable model, dubbed Trend Recommender (TrendRec), to forecast items’ future trend and generate trending item lists. Experiments on real-world datasets from various domains show that our TrendRec significantly outperforms the baselines, verifying our model’s effectiveness.

  Full text in ACM Digital Library

• RESA Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions
  by Norman Knyazev (Radboud University) and Harrie Oosterhuis (Radboud University).

  Most recommender systems (RecSys) do not provide an indication of confidence in their decisions. Therefore, they do not distinguish between recommendations of which they are certain, and those where they are not. Existing confidence methods for RecSys are either inaccurate heuristics, conceptually complex or very computationally expensive. Consequently, real-world RecSys applications rarely adopt these methods, and thus, provide no confidence insights in their behavior. In this work, we propose learned beta distributions (LBD) as a simple and practical recommendation method with an explicit measure of confidence. Our main insight is that beta distributions predict user preferences as probability distributions that naturally model confidence on a closed interval, yet can be implemented with the minimal model-complexity. Our results show that LBD maintains competitive accuracy to existing methods while also having a significantly stronger correlation between its accuracy and confidence. Furthermore, LBD has higher performance when applied to a high-precision targeted recommendation task. Our work thus shows that confidence in RecSys is possible without sacrificing simplicity or accuracy, and without introducing heavy computational complexity. Thereby, we hope it enables better insight into real-world RecSys and opens the door for novel future applications.

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• INDInvestigating the effects of incremental training on neural ranking models
  by Benedikt Schifferer (NVIDIA), Wenzhe Shi (ShareChat), Gabriel de Souza Pereira Moreira (NVIDIA), Even Oldridge (NVIDIA), Chris Deotte (NVIDIA), Gilberto Titericz (NVIDIA), Kazuki Onodera (NVIDIA), Praveen Dhinwa (ShareChat), Vishal Agrawal (ShareChat) and Chris Green (ShareChat).

  Recommender systems are an essential component of online systems, providing users with a personalized experience. Some recommendation scenarios such as social networks or news are very dynamic, with new items added continuously and the interest of users changing over time due to breaking news or popular events. Incremental training is a popular technique to keep recommender models up-to-date in those dynamic platforms. In this paper, we provide an empirical analysis of a large industry dataset from the Sharechat app MOJ, a social media platform for short videos, to answer relevant questions like – how often should I retrain the model? – do different models, features and dataset sizes benefit from incremental training? – Do all users and items benefit the same from incremental training?

  Full text in ACM Digital Library

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