A Generic Network Compression Framework for Sequential Recommender Systems

Sun Yang
Sun Yang
Yang Ming
Yang Ming
Wei Guoao
Wei Guoao
Liu Duo
Liu Duo

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July, 2020, pp. 1299-1308, 2020.

Cited by: 0|Bibtex|Views134|DOI:https://doi.org/10.1145/3397271.3401125
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Other Links: arxiv.org|dl.acm.org|dblp.uni-trier.de|academic.microsoft.com
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We propose a compressed sequential recommendation framework, termed as CpRec, where two generic model shrinking techniques are employed

Abstract:

Sequential recommender systems (SRS) have become the key technology in capturing user's dynamic interests and generating high-quality recommendations. Current state-of-the-art sequential recommender models are typically based on a sandwich-structured deep neural network, where one or more middle (hidden) layers are placed between the inpu...More

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Introduction
  • Sequential (a.k.a. session-based) recommender systems (SRS) have become a research hotspot in the recommendation eld. is is because user interaction behaviors in real-life scenarios o en exist in a form of chronological sequences.
  • Is is because user interaction behaviors in real-life scenarios o en exist in a form of chronological sequences
  • In such scenarios, traditional RS based on collaborative ltering [25] or content features [21] fail to model user’s dynamic interests and o er only sub-optimal performance.
  • As shown in Figure 1, the prediction accuracy of the sequential recommender model NextItNet [42] can be largely improved by increasing its model size, i.e., using a larger embedding dimension or a deeper network architecture.
  • NextItNet obtains more than 20% accuracy gains by increasing d from 64 to 512, along with about 3 times larger parameters
Highlights
  • Sequential (a.k.a. session-based) recommender systems (SRS) have become a research hotspot in the recommendation eld. is is because user interaction behaviors in real-life scenarios o en exist in a form of chronological sequences
  • Sequential recommender models based on recurrent neural networks (RNN) [14, 23] or convolutional neural network (CNN) (o en with dilated kernels) [42] have obtained state-of-the-art performance since these models are more powerful in capturing sequential dependencies in the interaction sequence
  • A well-known observation is that frequencies of items generally obey a long-tailed distribution [33, 42, 42], where some “head” items have a large number of user interactions, yet only a few interactions are available for the “tail” items
  • We have proposed CpRec, a exible & generic neural network compression framework for learning compact sequential recommender models
  • An important conclusion made from these results is that the commonly used recommender models are not compact at all
  • We expect CpRec to be valuable for existing Sequential recommender systems (SRS) based on deep neural networks
Methods
  • The authors present two main model compression techniques to improve the parameter e ciency of SRS.
  • An obvious di culty is that if the authors set adaptive (i.e.,variable-sized) embeddings to items, they cannot be directly trained by the typical sequential recommender model due to inconsistent dimensions of middle layers.
  • To this end, the authors perform dimension transformation by multiplying a projection matrix.
  • The transformation process is equivalent to a low-rank factorization given that the original large embedding matrix is reconstructed by two smaller matrices
Results
  • In order to evaluate the recommendation accuracy of CpRec, the authors randomly split all datasets into training (80%) and testing (20%) sets.
  • Following previous works [13, 14], the authors use the popular top-N metrics, including MRR@N (Mean Reciprocal Rank), HR@N (Hit Ratio) and NDCG@N (Normalized Discounted Cumulative Gain), where N is set to 5 and 20.
  • To evaluate the parameter e ciency, the authors Data Model.
  • MRR@5 HR@5 TT Params NextItNet Weishi.
  • Bo-NextItNet 0.1063 0.1766 66 34M
Conclusion
  • The authors have proposed CpRec, a exible & generic neural network compression framework for learning compact sequential recommender models.
  • CpRec signi cantly reduces parameter size in both the input and so max layer by leveraging the inherent long-tailed item distribution.
  • CpRec performs further compression by a series of layer-wise parameter sharing methods.
  • The authors expect CpRec to be valuable for existing SRS based on deep neural networks
Summary
  • Introduction:

    Sequential (a.k.a. session-based) recommender systems (SRS) have become a research hotspot in the recommendation eld. is is because user interaction behaviors in real-life scenarios o en exist in a form of chronological sequences.
  • Is is because user interaction behaviors in real-life scenarios o en exist in a form of chronological sequences
  • In such scenarios, traditional RS based on collaborative ltering [25] or content features [21] fail to model user’s dynamic interests and o er only sub-optimal performance.
  • As shown in Figure 1, the prediction accuracy of the sequential recommender model NextItNet [42] can be largely improved by increasing its model size, i.e., using a larger embedding dimension or a deeper network architecture.
  • NextItNet obtains more than 20% accuracy gains by increasing d from 64 to 512, along with about 3 times larger parameters
  • Methods:

    The authors present two main model compression techniques to improve the parameter e ciency of SRS.
  • An obvious di culty is that if the authors set adaptive (i.e.,variable-sized) embeddings to items, they cannot be directly trained by the typical sequential recommender model due to inconsistent dimensions of middle layers.
  • To this end, the authors perform dimension transformation by multiplying a projection matrix.
  • The transformation process is equivalent to a low-rank factorization given that the original large embedding matrix is reconstructed by two smaller matrices
  • Results:

    In order to evaluate the recommendation accuracy of CpRec, the authors randomly split all datasets into training (80%) and testing (20%) sets.
  • Following previous works [13, 14], the authors use the popular top-N metrics, including MRR@N (Mean Reciprocal Rank), HR@N (Hit Ratio) and NDCG@N (Normalized Discounted Cumulative Gain), where N is set to 5 and 20.
  • To evaluate the parameter e ciency, the authors Data Model.
  • MRR@5 HR@5 TT Params NextItNet Weishi.
  • Bo-NextItNet 0.1063 0.1766 66 34M
  • Conclusion:

    The authors have proposed CpRec, a exible & generic neural network compression framework for learning compact sequential recommender models.
  • CpRec signi cantly reduces parameter size in both the input and so max layer by leveraging the inherent long-tailed item distribution.
  • CpRec performs further compression by a series of layer-wise parameter sharing methods.
  • The authors expect CpRec to be valuable for existing SRS based on deep neural networks
Tables
  • Table1: Statistic of the evaluated datasets. ”M” and ”K” is short for million and kilo, ”t” is the length of interaction sequences. For ColdRec, the le and right values devided by ‘/’ denote the source and target dataset, respectively
  • Table2: Overall performance comparison, including recommendation accuracy, parameter e ciency (Params) , training time and inference speedup (evaluated by the generation of top-5 items). We omit the Params, Training Time (min) and Inference Speedup for GRU4Rec and Caser since they are not comparable to CpRec. MostPop returns item lists ranked by popularity. CpRec with cross-layer [<a class="ref-link" id="c18" href="#r18">18</a>], cross-block, adjacent-layer and adjacent-block parameter sharing is referred to CpRec-Cl, CpRec-Cb, CpRec-Al and CpRec-Ab respectively
  • Table3: Performance comparison w.r.t. how to apply the blockwise embedding decomposition. NextItNet that uses block-wise decomposition in the input layer, output layer and both are referred to Bi-NextItNet, Bo-NextItNet and Bio-NextItNet, respectively. B1NextItNet employs the standard low-rank decomposition (i.e., with only 1 block) in the input and so max layer inspired by [<a class="ref-link" id="c18" href="#r18">18</a>]. Note that for clarity only the parameters in the input and output matrices are reported in the Params Column. TT is short for training time (unit: min). e inference speedup is simply omitted due to similar results as in Table 2
  • Table4: e impact of layer-wise parameter sharing strategies. NextItNet with cross-layer, cross-block, adjacent-layer and adjacent-block parameter sharing is denoted by Cl-NextItNet, CbNextItNet, Al-NextItNet, Ab-NextItNet, respectively. Note for clarity only the parameters in the middle layers are shown in the Params Column
  • Table5: e e ect of adaptive embedding decomposition applied to GRU4Rec. TT is short for training time (unit: min)
  • Table6: CpRec vs. NextItNet on the transfer learning task. Note that our evaluation strictly follows [<a class="ref-link" id="c41" href="#r41">41</a>]. MRR@5 & HR@5 are the netuned accuracy, whereas Params and training time are evaluated on the pre-trained model, which is computationally more expensive than the netuned model
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Related work
  • 2.1 DNN-based SRS

    Recently, deep neural networks (DNNs) have brought great improvements for SRS and almost dominate this eld. us far, three types of DNN models have been explored for SRS. Among them, Recurrent Neural Networks (RNNs) are o en a natural choice for modeling sequence data [8]. GRU4Rec[14, 29] is regarded as the seminal work that rstly applied gated recurrent units (GRU) architecture for sequential recommendation tasks. Inspired by them, a variety of RNN variants have been proposed to address the sequential recommendation problems, such as personalized SRS with hierarchical RNN [39], content- & context-based SRS [7, 26], data augmentation-based SRS [29]. While e ective, these RNN-based models seriously depend on the hidden state of the entire past, which cannot take full advantage of modern parallel processing resources [42], such as GPU/TPU. By contrast, convolutional neural networks (CNNs) and pure a ention-based models do not have such limitations since the entire sequence is already available during training. In addition, CNN and a ention-based sequential models can perform be er than RNN recommenders since much more hidden layers can be stacked by the residual block architecture [11]. To be more speci c, [42] proposed a CNN-based generative model called NextItNet, which employs a stack of dilated convolutional layers to increase the receptive eld when modeling long-range sequences. Likewise, self-a ention based models, such as SASRec [17] and BERT4Rec [28] also obtained competitive results. Compared with NextItNet, the self-a ention mechanism is computationally more expensive since calculating self-a ention of all timesteps requires quadratic complexity and memory.
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