Session 16: Multimedia Recommendation

Date: Friday September 22, 2:00 PM – 3:20 PM (GMT+8)
Room: Hall 406D
Session Chair: Justin Basilico
Parallel with: Session 15: Cross-domain Recommendation

• RESUncovering User Interest from Biased and Noised Watch Time in Video Recommendation
  by Haiyuan Zhao (Renmin University of China), Lei Zhang (Renmin University of China), Jun Xu (Renmin University of China), Guohao Cai (Huawei Noah’s ark lab), Zhenhua Dong (Huawei Noah’s ark lab) and Ji-Rong Wen (Renmin University of China).

  In micro-video recommendation scenarios, watch time is commonly adopted as an indicator of users’ interest. However, watch time is not only determined by the matching of users’ interests but is affected by other factors. These factors mainly lie in two folds: on the one hand, users tend to spend more time on those charming videos with the growth of the duration (i.e., video length), named as duration bias; on the other hand, it costs people a period of time to judge whether they like the video, named as noisy watching. Consequently, the existence of duration bias and noisy watching make watch time an inadequate label for training a reliable recommendation model. Moreover, current methods focus only on the duration bias and ignore the duration noise, so they do not really uncover the user interest from watch time. In this study, we first analyze the generation mechanism of users’ watch time in a unified causal viewpoint. Unlike current methods, which only notice the duration bias in watch time, we considered the watch time as a mixture of the user’s actual interest, the duration biased watch time, and the noisy watch time. To mitigate both the duration bias and noisy watching, we propose Debiased and Denoised watch time Correction (D$^2$Co), which can be divided into two steps: First, we employ a duration-wise Gaussian Mixture Model plus frequency-weighted moving average for estimating the bias and noise terms; Then we utilize a sensitivity-controlled correction function to separate the user interest from the watch time, which is robust to the estimation error of bias and noise terms. The experiments on two public video recommendation datasets indicate the effectiveness of the proposed method.

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• RESUnderstanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
  by Yunzhu Pan (UESTC), Chen Gao (Tsinghua University), Yang Song (Kuaishou Inc.), Kun Gai (Unaffiliated), Depeng Jin (Department of Electronic Engineering, Tsinghua University) and Yong Li (Tsinghua University).

  Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditional sequential recommendation always mainly considers the collected positive feedback such as click, purchase, etc. However, in short-video platforms such as TikTok, video viewing behavior may not always represent positive feedback. Specifically, the videos are played automatically, and users passively receive the recommended videos. In this new scenario, users passively express negative feedback by skipping over videos they do not like, which provides valuable information about their preferences. Different from the negative feedback studied in traditional recommender systems, this passive-negative feedback can reflect users’ interests and serve as an important supervision signal in extracting users’ preferences. Therefore, it is essential to carefully design and utilize it in this novel recommendation scenario. In this work, we first conduct analyses based on a large-scale real-world short-video behavior dataset and illustrate the significance of leveraging passive feedback. We then propose a novel method that deploys the sub-interest encoder, which incorporates positive feedback and passive-negative feedback as supervision signals to learn the user’s current active sub-interest. Moreover, we introduce an adaptive fusion layer to integrate various sub-interests effectively. To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback – passive-negative feedback and traditional randomly-sampled negative feedback. The experiments on two large-scale datasets verify that the proposed method can significantly outperform state-of-the-art approaches. The codes and collected datasets are anonymously released at https:// anonymous.4open.science/ r/ SINE-2047/ to benefit the community.

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• INDPersonalised Recommendations for the BBC iPlayer: Initial approach and current challenges
  by Benjamin R. Clark (British Broadcasting Corporation), Kristine Grivcova (British Broadcasting Corporation), Polina Proutskova (British Broadcasting Corporation) and Duncan M. Walker (British Broadcasting Corporation).

  BBC iPlayer is one of the most important digital products of the BBC, offering live and on-demand television for audiences in the UK with over 10 million weekly active users. The BBC’s role as a public service broadcaster, broadcasting over traditional linear channels as well as online presents a number of challenges for a recommender system. In addition to having substantially different objectives to a commercial service, we show that the diverse content offered by the BBC including news and sport, factual, drama and live events lead to a catalogue with a diversity of consumption patterns, depending on genre. Our research shows that simple models represent strong baselines in this system. We discuss our initial attempts to improve upon these baselines, and conclude with our current challenges.

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• REPReproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasures
  by Pasquale Lops (University of Bari), Elio Musacchio (Università degli Studi di Bari Aldo Moro), Cataldo Musto (Dipartimento di Informatica – University of Bari), Marco Polignano (Università degli Studi di Bari Aldo Moro), Antonio Silletti (Dipartimento di Informatica – University of Bari) and Giovanni Semeraro (Dipartimento di Informatica – University of Bari)

  Reproducibility is an important requirement for scientific progress, and the lack of reproducibility for a large amount of published research can hinder the progress over the state-of-the-art. This concerns several research areas, and recommender systems are witnessing the same reproducibility crisis. Even solid works published at prestigious venues might not be reproducible for several reasons: data might not be public, source code for recommendation algorithms might not be available or well documented, and evaluation metrics might be computed using parameters not explicitly provided. In addition, recommendation pipelines are becoming increasingly complex due to the use of deep neural architectures or representations for multimodal side information involving text, images, audio, or video. This makes the reproducibility of experiments even more challenging. In this work, we describe an extension of an already existing open-source recommendation framework, called ClayRS, with the aim of providing the foundation for future reproducibility of recommendation processes involving images as side information. This extension, called ClayRS Can See, is the starting point for reproducing state-of-the-art recommendation algorithms exploiting images. We have provided our implementation of one of these algorithms, namely VBPR – Visual Bayesian Personalized Ranking from Implicit Feedback, and we have discussed all the issues related to the reproducibility of the study to deeply understand the main traps and pitfalls, along with solutions to deal with such complex environments. We conclude the work by proposing a checklist for recommender systems reproducibility as a guide for the research community.

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