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Found 15 papers, 6 papers with code
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation
(2) generate a post including selected products via the MGenNet (Multi-Generator Network).
Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights
Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, predominantly depend on sparse ID features in their models.
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach
However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions.
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order
Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline.
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
To alleviate this issue, we propose to extract knowledge from the \textit{super-domain} that contains web-scale and long-time impression data, and further assist the online recommendation task (downstream task).
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
We further show that such a process is equivalent to adding an adversarial perturbation to the model input, and thereby name our proposed approach as an the Adversarial Gradient Driven Exploration (AGE).
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).
CAN: Feature Co-Action for Click-Through Rate Prediction
For example, a simple attempt to learn the combination of feature A and feature B <A, B> as the explicit cartesian product representation of new features can outperform previous implicit feature interaction models including factorization machine (FM)-based models and their variations.
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles
Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders
In this work, we improve the WAE for response generation.
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation
Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing
There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries.
Learning towards Abstractive Timeline Summarization
Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.
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Timeline Summarization
on MTS
GSN: A Graph-Structured Network for Multi-Party Dialogues
Existing neural models for dialogue response generation assume that utterances are sequentially organized.
