Session 5: Sequential Recommendation 1

Date: Wednesday September 20, 4:05 PM – 5:25 PM (GMT+8)
Room: Hall 406CX
Session Chair: Craig Macdonald
Parallel with: Session 6: Graphs

• RESDistribution-based Learnable Filters with Side Information for Sequential Recommendation
  by Haibo Liu (School of Cyber Security and Computer, HeBei university), Zhixiang Deng (School of Cyber Security and Computer, HeBei university), Liang Wang (School of Cyber Security and Computer, HeBei university), Jinjia Peng (School of Cyber Security and Computer, HeBei university) and Shi Feng (School of Computer Science & Engineering, Northeastern University).

  Sequential Recommendation aims to predict the next item by mining out the dynamic preference from user previous interactions. However, most methods represent each item as a single fixed vector, which is incapable of capturing the uncertainty of item-item transitions that result from time-dependent and multifarious interests of users. Besides, they fail to effectively exploit side information that helps to better express user preferences. At last, the noise in user’s access sequence, which is due to accidental clicks, can interfere with the next item prediction and lead to lower recommendation performance. To deal with these issues, we propose DLFS-Rec, a novel model that combines Distribution-based Learnable Filters with Side information for sequential Recommendation. Specifically, items and their side information are represented by stochastic Gaussian distribution, which is described by mean and covariance embeddings, and then the corresponding embeddings are fused to generate a final representation for each item. To attenuate noise, stacked learnable filter layers are applied to smooth the fused embeddings. The similarities between the distributions inferred from the last filter layer and candidates are measured by 2-Wasserstein distance for generating recommendation list. Extensive experiments on four public real-world datasets demonstrate the superiority of the proposed model over state-of-the-art baselines, especially on cold start users and items.

  Full text in ACM Digital Library

• RESReciprocal Sequential Recommendation
  by Bowen Zheng (Renmin University of China), Yupeng Hou (Renmin University of China), Wayne Xin Zhao (Renmin University of China), Yang Song (BOSS Zhipin) and Hengshu Zhu (BOSS Zhipin).

  Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties. Although dynamic user modeling has been well-studied in sequential recommender systems, existing solutions are developed in a user-oriented manner. Therefore, it is non-trivial to adapt sequential recommendation algorithms to reciprocal recommendation. In this paper, we formulate RRS as a distinctive sequence matching task, and further propose a new approach ReSeq for RRS, which is short for Reciprocal Sequential recommendation. To capture duel-perspective matching, we propose to learn fine-grained sequence similarities by co-attention mechanism across different time steps. Further, to improve the inference efficiency, we introduce the self-distillation technique to distill knowledge from the fine-grained matching module into the more efficient student module. In the deployment stage, only the efficient student module is used, greatly speeding up the similarity computation. Extensive experiments on five real-world datasets from two scenarios demonstrate the effectiveness and efficiency of the proposed method. Our code is available at https://anonymous.4open.science/r/ReSeq/.

  Full text in ACM Digital Library

• RESSTRec: Sparse Transformer for Sequential Recommendations
  by Chengxi Li (City University of Hong Kong), Xiangyu Zhao (City University of Hong Kong), Yejing Wang (City University of Hong Kong), Qidong Liu (Xi’an Jiaotong University, City University of Hong Kong), Wanyu Wang (City University of Hong Kong), Yiqi Wang (Michigan State University), Lixin Zou (Wuhan University), Wenqi Fan (The Hong Kong Polytechnic University) and Qing Li (The Hong Kong Polytechnic University).

  With the rapid evolution of transformer architectures, an increasing number of researchers are exploring their application in sequential recommender systems (SRSs). Compared with the former SRS models, the transformer-based models get promising performance on SRS tasks. Existing transformer-based SRS frameworks, however, retain the vanilla attention mechanism, which calculates the attention scores between all item-item pairs in each layer, i.e., item interactions. Consequently, redundant item interactions may downgrade the inference speed and cause high memory costs for the model. In this paper, we first identify the sparse information phenomenon in transformer-based SRS scenarios and propose an efficient model, i.e., Sparse Transformer sequential Recommendation model (STRec). First, we devise a cross-attention-based sparse transformer for efficient sequential recommendation. Then, a novel sampling strategy is derived to preserve the necessary interactions. Extensive experimental results validate the effectiveness of our framework, which could outperform the state-of-the-art accuracy while reducing 54% inference time and 70% memory cost. Besides, we provide massive extended experiments to further investigate the property of our framework. Our code is available to ease reproducibility.

  Full text in ACM Digital Library

• INDTrack Mix Generation on Music Streaming Services using Transformers
  by Walid Bendada (Deezer Research), Théo Bontempelli (Deezer Research), Mathieu Morlon (Deezer Research), Benjamin Chapus (Deezer Research), Thibault Cador (Deezer Research), Thomas Bouabça (Deezer Research) and Guillaume Salha-Galvan (Deezer Research).

  This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates “mix” playlists inspired by initial music tracks, allowing users to discover music similar to their favorite content. To generate these mixes, we consider a Transformer model trained on millions of track sequences from user playlists. In light of the growing popularity of Transformers in recent years, we analyze the advantages, drawbacks, and technical challenges of using such a model for mix generation on the service, compared to a more traditional collaborative filtering approach. Since its release, Track Mix has been generating playlists for millions of users daily, enhancing their music discovery experience on Deezer.

  Full text in ACM Digital Library

Back to program