8 research outputs found
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
Quality product descriptions are critical for providing competitive customer
experience in an e-commerce platform. An accurate and attractive description
not only helps customers make an informed decision but also improves the
likelihood of purchase. However, crafting a successful product description is
tedious and highly time-consuming. Due to its importance, automating the
product description generation has attracted considerable interests from both
research and industrial communities. Existing methods mainly use templates or
statistical methods, and their performance could be rather limited. In this
paper, we explore a new way to generate the personalized product description by
combining the power of neural networks and knowledge base. Specifically, we
propose a KnOwledge Based pErsonalized (or KOBE) product description generation
model in the context of e-commerce. In KOBE, we extend the encoder-decoder
framework, the Transformer, to a sequence modeling formulation using
self-attention. In order to make the description both informative and
personalized, KOBE considers a variety of important factors during text
generation, including product aspects, user categories, and knowledge base,
etc. Experiments on real-world datasets demonstrate that the proposed method
out-performs the baseline on various metrics. KOBE can achieve an improvement
of 9.7% over state-of-the-arts in terms of BLEU. We also present several case
studies as the anecdotal evidence to further prove the effectiveness of the
proposed approach. The framework has been deployed in Taobao, the largest
online e-commerce platform in China.Comment: KDD 2019 Camera-ready. Website:
https://sites.google.com/view/kobe201
ANALISIS SEARCH ENGINE OPTIMIZATION (SEO) PADA UMKM DOA IBU KOSMETIK DI TOKOPEDIA
The existence of social restrictions during the COVID-19 pandemic has had many impacts, one of which is limiting direct sales of SMEs. One of them is Doa Ibu Kosmetik which sells various personal needs like cosmetics and body care. Doa Ibu Kosmetik opens a digital shop through Tokopedia to encourage sales. Search Engine Optimization (SEO) method was implemented in the Tokopedia account to compete with others. The method used to optimize the shop page is to upgrade the shop's status to Power Merchant and activate the features provided by Tokopedia. Product page optimization is carried out by adding titles, prices, descriptions, and product photos so that they comply with Tokopedia's recommendations and can compete with other sellers. The TopAds feature, which is Tokopedia's paid advertising feature, has also been implemented to help increase traffic on the Tokopedia Doa Ibu Kosmetik page. The research results show that optimization on Tokopedia can increase visits and sales
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
Poet: Product-oriented Video Captioner for E-commerce
In e-commerce, a growing number of user-generated videos are used for product
promotion. How to generate video descriptions that narrate the user-preferred
product characteristics depicted in the video is vital for successful
promoting. Traditional video captioning methods, which focus on routinely
describing what exists and happens in a video, are not amenable for
product-oriented video captioning. To address this problem, we propose a
product-oriented video captioner framework, abbreviated as Poet. Poet firstly
represents the videos as product-oriented spatial-temporal graphs. Then, based
on the aspects of the video-associated product, we perform knowledge-enhanced
spatial-temporal inference on those graphs for capturing the dynamic change of
fine-grained product-part characteristics. The knowledge leveraging module in
Poet differs from the traditional design by performing knowledge filtering and
dynamic memory modeling. We show that Poet achieves consistent performance
improvement over previous methods concerning generation quality, product
aspects capturing, and lexical diversity. Experiments are performed on two
product-oriented video captioning datasets, buyer-generated fashion video
dataset (BFVD) and fan-generated fashion video dataset (FFVD), collected from
Mobile Taobao. We will release the desensitized datasets to promote further
investigations on both video captioning and general video analysis problems.Comment: 10 pages, 3 figures, to appear in ACM MM 2020 proceeding
Comprehensive Information Integration Modeling Framework for Video Titling
In e-commerce, consumer-generated videos, which in general deliver consumers'
individual preferences for the different aspects of certain products, are
massive in volume. To recommend these videos to potential consumers more
effectively, diverse and catchy video titles are critical. However,
consumer-generated videos seldom accompany appropriate titles. To bridge this
gap, we integrate comprehensive sources of information, including the content
of consumer-generated videos, the narrative comment sentences supplied by
consumers, and the product attributes, in an end-to-end modeling framework.
Although automatic video titling is very useful and demanding, it is much less
addressed than video captioning. The latter focuses on generating sentences
that describe videos as a whole while our task requires the product-aware
multi-grained video analysis. To tackle this issue, the proposed method
consists of two processes, i.e., granular-level interaction modeling and
abstraction-level story-line summarization. Specifically, the granular-level
interaction modeling first utilizes temporal-spatial landmark cues, descriptive
words, and abstractive attributes to builds three individual graphs and
recognizes the intra-actions in each graph through Graph Neural Networks (GNN).
Then the global-local aggregation module is proposed to model inter-actions
across graphs and aggregate heterogeneous graphs into a holistic graph
representation. The abstraction-level story-line summarization further
considers both frame-level video features and the holistic graph to utilize the
interactions between products and backgrounds, and generate the story-line
topic of the video. We collect a large-scale dataset accordingly from
real-world data in Taobao, a world-leading e-commerce platform, and will make
the desensitized version publicly available to nourish further development of
the research community...Comment: 11 pages, 6 figures, to appear in KDD 2020 proceeding
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
Existing neural methods for data-to-text generation are still struggling to
produce long and diverse texts: they are insufficient to model input data
dynamically during generation, to capture inter-sentence coherence, or to
generate diversified expressions. To address these issues, we propose a
Planning-based Hierarchical Variational Model (PHVM). Our model first plans a
sequence of groups (each group is a subset of input items to be covered by a
sentence) and then realizes each sentence conditioned on the planning result
and the previously generated context, thereby decomposing long text generation
into dependent sentence generation sub-tasks. To capture expression diversity,
we devise a hierarchical latent structure where a global planning latent
variable models the diversity of reasonable planning and a sequence of local
latent variables controls sentence realization. Experiments show that our model
outperforms state-of-the-art baselines in long and diverse text generation.Comment: To appear in EMNLP 201