Self-Supervised learning for Conversational Recommendation

Li, Shuokai; Xie, Rong; Zhu, Yongchun; Zhuang, Fuzhen; Tang, Zhenwei; Zhao, Wayne Xin; He, Qing

doi:10.1016/j.ipm.2022.103067

Cited by 14 publications

(7 citation statements)

References 41 publications

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“…The backbone of DNAGPT is a transformer-based [29] auto-regressive [30] decoder with the masked self-attention [31] module. To better deal with numerical information, we pre-train the DNA sequence and numerical property end to end in a single model.…”

Section: Dnagpt Architecture 21 Model Structurementioning

confidence: 99%

DNAGPT: A Generalized Pre-trained Tool for Multiple DNA Sequence Analysis Tasks

Daoan

Zhang

et al. 2023

Preprint

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The success of the GPT series proves that GPT can extract general information from sequences, thereby benefiting all downstream tasks. This motivates us to use pre-trained models to explore the hidden information in DNA sequences. However, data and task requirements in DNA sequence analysis are complexity and diversity as DNA relevant data includes different types of information, such as sequences, expression levels, etc, while there is currently no model specifically designed for these characteristics. Hereby, we present DNAGPT, a generalized foundation model pre-trained on over 10 billion base pairs from 9 species which can be fine-tuned for any DNA sequence analysis task. Our model can simultaneously process or output DNA sequences and numbers. In addition, our unique token design allows users to design prompts according to their own task requirements, making it applicable to any type of task. We have evaluated our model on classification, regression, and generation tasks. We demonstrate that DNAGPT benefits from pre-training, and therefore can bring performance gains to any downstream task. Our model is not only a new attempt in the field of genomes analysis, but also provides a new direction for the application of foundation models in biology.

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Section: Dnagpt Architecture 21 Model Structurementioning

confidence: 99%

DNAGPT: A Generalized Pre-trained Tool for Multiple DNA Sequence Analysis Tasks

Daoan

Zhang

et al. 2023

Preprint

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“…Conversational search (CS) is applied in many fields such as recommendation systems, e-health and personality recognition ( Aliannejadi et al, 2020 ; Velicia-Martin et al, 2021 ; Shen et al., 2023 ). Deep learning solutions for CS have replaced more traditional rule-based approaches ( Onal et al, 2018 ; Gao, Galley & Li, 2018 ; Li et al., 2022 ). A main challenge is how to ensure conversational context-awareness ( Vtyurina et al, 2017 ).…”

Section: Literature Reviewmentioning

confidence: 99%

ECAsT: a large dataset for conversational search and an evaluation of metric robustness

Al-Thani

Jansen

Elsayed

2023

PeerJ Computer Science

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The Text REtrieval Conference Conversational assistance track (CAsT) is an annual conversational passage retrieval challenge to create a large-scale open-domain conversational search benchmarking. However, as of yet, the datasets used are small, with just more than 1,000 turns and 100 conversation topics. In the first part of this research, we address the dataset limitation by building a much larger novel multi-turn conversation dataset for conversation search benchmarking called Expanded-CAsT (ECAsT). ECAsT is built using a multi-stage solution that uses a combination of conversational query reformulation and neural paraphrasing and also includes a new model to create multi-turn paraphrases. The meaning and diversity of paraphrases are evaluated with human and automatic evaluation. Using this methodology, we produce and release to the research community a conversational search dataset that is 665% more extensive in terms of size and language diversity than is available at the time of this study, with more than 9,200 turns. The augmented dataset not only provides more data but also more language diversity to improve conversational search neural model training and testing. In the second part of the research, we use ECAsT to assess the robustness of traditional metrics for conversational evaluation used in CAsT and identify its bias toward language diversity. Results show the benefits of adding language diversity for improving the collection of pooled passages and reducing evaluation bias. We found that introducing language diversity via paraphrases returned up to 24% new passages compared to only 2% using CAsT baseline.

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“…In contrastive learning, a pair of positive and negative examples are used to learn representations by comparing them [30]. Typically, positive-negative example pairs consist of semantically related neighbors.…”

Section: Contrastive Learningmentioning

confidence: 99%

Intent with knowledge-aware multiview contrastive learning for recommendation

Tao,

Qiu,

Cao

et al. 2023

Complex Intell. Syst.

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User–item interactions on e-commerce platforms involve various intents, such as browsing and purchasing, which require fine-grained intent recognition. Existing recommendation methods incorporate latent intent into user–item interactions; however, they overlook important considerations. First, they fail to integrate intents with semantic information in knowledge graphs, neglecting intent interpretability. Second, they do not exploit the structural information from multiple views of latent intents in user–item interactions. This study established the intent with knowledge-aware multiview contrastive learning (IKMCL) model for explanation in recommendation systems. The proposed IKMCL model converts latent intent into fine-grained intent, calculates intent weights, mines latent semantic information, and learns the representation of user–item interactions through multiview intent contrastive learning. In particular, we combined fine-grained intents with a knowledge graph to calculate intent weights and capture intent semantics. The IKMCL model performs multiview intent contrastive learning at both coarse-grained and fine-grained levels to extract semantic relationships in user–item interactions and provide intent recommendations in structural and semantic views. In addition, an intent-relational path was designed based on multiview contrastive learning, enabling the capture of semantic information from latent intents and personalized item recommendations with interpretability. Experimental results using large benchmark datasets indicated that the proposed model outperformed other advanced methods, significantly improving recommendation performance.

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Self-Supervised learning for Conversational Recommendation

Cited by 14 publications

References 41 publications

DNAGPT: A Generalized Pre-trained Tool for Multiple DNA Sequence Analysis Tasks

DNAGPT: A Generalized Pre-trained Tool for Multiple DNA Sequence Analysis Tasks

ECAsT: a large dataset for conversational search and an evaluation of metric robustness

Intent with knowledge-aware multiview contrastive learning for recommendation

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