Breaking the Softmax Bottleneck for Sequential Recommender Systems with Dropout and Decoupling

• URL: http://arxiv.org/abs/2110.05409v1
• Date: Mon, 11 Oct 2021 16:52:23 GMT
• Title: Breaking the Softmax Bottleneck for Sequential Recommender Systems with Dropout and Decoupling
• Authors: Ying-Chen Lin
• Abstract summary: We show that there are more aspects to the Softmax bottleneck in SBRSs. We propose a simple yet effective method, Dropout and Decoupling (D&D), to alleviate these problems. Our method significantly improves the accuracy of a variety of Softmax-based SBRS algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Softmax bottleneck was first identified in language modeling as a theoretical limit on the expressivity of Softmax-based models. Being one of the most widely-used methods to output probability, Softmax-based models have found a wide range of applications, including session-based recommender systems (SBRSs). Softmax-based models consist of a Softmax function on top of a final linear layer. The bottleneck has been shown to be caused by rank deficiency in the final linear layer due to its connection with matrix factorization. In this paper, we show that there are more aspects to the Softmax bottleneck in SBRSs. Contrary to common beliefs, overfitting does happen in the final linear layer, while it is often associated with complex networks. Furthermore, we identified that the common technique of sharing item embeddings among session sequences and the candidate pool creates a tight-coupling that also contributes to the bottleneck. We propose a simple yet effective method, Dropout and Decoupling (D&D), to alleviate these problems. Our experiments show that our method significantly improves the accuracy of a variety of Softmax-based SBRS algorithms. When compared to other computationally expensive methods, such as MLP and MoS (Mixture of Softmaxes), our method performs on par with and at times even better than those methods, while keeping the same time complexity as Softmax-based models.

Related papers

• MultiMax: Sparse and Multi-Modal Attention Learning [60.49318008131978]
  SoftMax is a ubiquitous ingredient of modern machine learning algorithms. We show that sparsity can be achieved by a family of SoftMax variants, but they often require an alternative loss function and do not preserve multi-modality. We propose MultiMax, which adaptively modulates the output distribution according to input entry range.
  arXiv  Detail & Related papers  (2024-06-03T10:51:43Z)
• A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning [2.8266810371534152]
  Discnative deep learning models with a linear+softmax final layer have a problem. Latent space only predicts the conditional probabilities $p(Y|X)$ but not the full joint distribution $p(Y,X)$. This exacerbates model over-confidence impacting many problems, such as hallucinations, confounding biases, and dependence on large datasets.
  arXiv  Detail & Related papers  (2024-04-27T18:41:32Z)
• ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters [14.029865087214436]
  Self-attention mechanism sets transformer-based large language model (LLM) apart from convolutional and recurrent neural networks. achieving real-time LLM inference on silicon is challenging due to the extensively used Softmax in self-attention. We propose Constant Softmax (ConSmax), a software- hardware co-design as an efficient Softmax alternative.
  arXiv  Detail & Related papers  (2024-01-31T17:52:52Z)
• To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders [48.8643117818312]
  In this study, we identify a major source of the problem: the single hidden state embedding and static item embeddings in the output softmax layer. We adapt the recently-proposed softmax alternatives such as softmax-CPR to sequential recommendation tasks and demonstrate that the new softmax architectures unleash the capability of the neural encoder on learning when to copy and when to exclude the items from the input sequence.
  arXiv  Detail & Related papers  (2023-10-21T18:04:04Z)
• r-softmax: Generalized Softmax with Controllable Sparsity Rate [11.39524236962986]
  We propose r-softmax, a modification of the softmax, outputting sparse probability distribution with controllable sparsity rate. We show on several multi-label datasets that r-softmax outperforms other sparse alternatives to softmax and is highly competitive with the original softmax.
  arXiv  Detail & Related papers  (2023-04-11T14:28:29Z)
• Spectral Aware Softmax for Visible-Infrared Person Re-Identification [123.69049942659285]
  Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Existing methods still follow the softmax loss training paradigm, which is widely used in single-modality classification tasks. We propose the spectral-aware softmax (SA-Softmax) loss, which can fully explore the embedding space with the modality information.
  arXiv  Detail & Related papers  (2023-02-03T02:57:18Z)
• Softmax-free Linear Transformers [90.83157268265654]
  Vision transformers (ViTs) have pushed the state-of-the-art for visual perception tasks. Existing methods are either theoretically flawed or empirically ineffective for visual recognition. We propose a family of Softmax-Free Transformers (SOFT)
  arXiv  Detail & Related papers  (2022-07-05T03:08:27Z)
• Enhancing Classifier Conservativeness and Robustness by Polynomiality [23.099278014212146]
  We show howconditionality can remedy the situation. A directly related, simple, yet important technical novelty we subsequently present is softRmax. We show that two aspects of softRmax, conservativeness and inherent robustness, lead to adversarial regularization.
  arXiv  Detail & Related papers  (2022-03-23T19:36:19Z)
• Sparse-softmax: A Simpler and Faster Alternative Softmax Transformation [2.3813678058429626]
  The softmax function is widely used in artificial neural networks for the multiclass classification problems. In this paper, we provide an empirical study on a simple and concise softmax variant, namely sparse-softmax, to alleviate the problem that occurred in traditional softmax in terms of high-dimensional classification problems.
  arXiv  Detail & Related papers  (2021-12-23T09:53:38Z)
• SOFT: Softmax-free Transformer with Linear Complexity [112.9754491864247]
  Vision transformers (ViTs) have pushed the state-of-the-art for various visual recognition tasks by patch-wise image tokenization followed by self-attention. Various attempts on approximating the self-attention with linear complexity have been made in Natural Language Processing. We identify that their limitations are rooted in keeping the softmax self-attention during approximations. For the first time, a softmax-free transformer or SOFT is proposed.
  arXiv  Detail & Related papers  (2021-10-22T17:57:29Z)
• Sparse Attention with Linear Units [60.399814410157425]
  We introduce a novel, simple method for achieving sparsity in attention: we replace the softmax activation with a ReLU. Our model, which we call Rectified Linear Attention (ReLA), is easy to implement and more efficient than previously proposed sparse attention mechanisms. Our analysis shows that ReLA delivers high sparsity rate and head diversity, and the induced cross attention achieves better accuracy with respect to source-target word alignment.
  arXiv  Detail & Related papers  (2021-04-14T17:52:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.