123 research outputs found
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
Designing an e-commerce recommender system that serves hundreds of millions
of active users is a daunting challenge. From a human vision perspective,
there're two key factors that affect users' behaviors: items' attractiveness
and their matching degree with users' interests. This paper proposes Telepath,
a vision-based bionic recommender system model, which understands users from
such perspective. Telepath is a combination of a convolutional neural network
(CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its
CNN subnetwork simulates the human vision system to extract key visual signals
of items' attractiveness and generate corresponding activations. Its RNN and
DNN subnetworks simulate cerebral cortex to understand users' interest based on
the activations generated from browsed items. In practice, the Telepath model
has been launched to JD's recommender system and advertising system. For one of
the major item recommendation blocks on the JD app, click-through rate (CTR),
gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71%
respectively. For several major ads publishers of JD demand-side platform, CTR,
GMV and return on investment have increased 6.58%, 61.72% and 65.57%
respectively by the first launch, and further increased 2.95%, 41.75% and
41.37% respectively by the second launch.Comment: 8 pages, 11 figures, 1 tabl
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
Multi-label recognition (MLR) with incomplete labels is very challenging.
Recent works strive to explore the image-to-label correspondence in the
vision-language model, \ie, CLIP, to compensate for insufficient annotations.
In spite of promising performance, they generally overlook the valuable prior
about the label-to-label correspondence. In this paper, we advocate remedying
the deficiency of label supervision for the MLR with incomplete labels by
deriving a structured semantic prior about the label-to-label correspondence
via a semantic prior prompter. We then present a novel Semantic Correspondence
Prompt Network (SCPNet), which can thoroughly explore the structured semantic
prior. A Prior-Enhanced Self-Supervised Learning method is further introduced
to enhance the use of the prior. Comprehensive experiments and analyses on
several widely used benchmark datasets show that our method significantly
outperforms existing methods on all datasets, well demonstrating the
effectiveness and the superiority of our method. Our code will be available at
https://github.com/jameslahm/SCPNet.Comment: Accepted by IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR) 202
Probing Product Description Generation via Posterior Distillation
In product description generation (PDG), the user-cared aspect is critical
for the recommendation system, which can not only improve user's experiences
but also obtain more clicks. High-quality customer reviews can be considered as
an ideal source to mine user-cared aspects. However, in reality, a large number
of new products (known as long-tailed commodities) cannot gather sufficient
amount of customer reviews, which brings a big challenge in the product
description generation task. Existing works tend to generate the product
description solely based on item information, i.e., product attributes or title
words, which leads to tedious contents and cannot attract customers
effectively. To tackle this problem, we propose an adaptive posterior network
based on Transformer architecture that can utilize user-cared information from
customer reviews. Specifically, we first extend the self-attentive Transformer
encoder to encode product titles and attributes. Then, we apply an adaptive
posterior distillation module to utilize useful review information, which
integrates user-cared aspects to the generation process. Finally, we apply a
Transformer-based decoding phase with copy mechanism to automatically generate
the product description. Besides, we also collect a large-scare Chinese product
description dataset to support our work and further research in this field.
Experimental results show that our model is superior to traditional generative
models in both automatic indicators and human evaluation
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