A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201

Semantic web for next generation of e-commerce

Web technology left a significant impact for business transaction.The role of buyers and vendors has been replaced by informative websites where the available information of products and services could improve supply chain and delivery cycles.As the market segment grows, the need of having organized and thoughtful web content is increasing.Search functions using keyword-based search are known for its inability for the machine to interpret different terminology with the same meaning.Information needs to be structured for parametric search to locate products with certain combination of traits.Ontology is the solution to structure semantic of product data.It allows computer to process content with meaning for human based consensual terminologies.Ontology provides a shared platform and common understanding of a domain that can be communicated between user and application systems.The purpose of this paper is to highlight the importance of exploiting ontology based e-commerce for Semantic Web. The ontology is mediator for software agents to communicate and exchange data.These agents can search products with certain traits, negotiate products or automatically configure product or services according to the required specifications.The semantic combination of product data elevate full potential of e-commerce and development of many specialized reasoning services bring full power of Semantic Web Based E-Commerce

he true complexity of product representation in the semantic web

The ontological representation of products and services is a core challenge on the road to business applications for the Semantic Web. This will not only help search engines provide more precise product search for human users, but can be expected to support a much higher degree of business process automation in general, especially in all tasks that involve content integration. In industrial data interchange between business partners, the state of the art is the use of common XML schema definitions (e.g. BMEcat) for the representation of structure and the use of classification schemes (e.g. UNSPSC or eCl@ss) for the representation of product semantics. This current practice, however, takes place in well-defined contexts known to both the publisher of data and the recipient, which allows even the usage of the same standard with varying semantics in distinct settings. In a Semantic Web context in contrast, the same document must be machine-readable (1) by a huge number of different partners (2) for a multiplicity of purposes. In other words, the data recipient and the data usage are not predetermined, which makes it much more difficult to reach consensus e.g. about suitable product classes. This paper develops the requirements for product representation in the Semantic Web and evaluates existing alternatives

Semantic and Syntactic Matching of Heterogeneous e-Catalogues

In e-procurement, companies use e-catalogues to exchange product infor-mation with business partners. Matching e-catalogues with product requests helps the suppliers to identify the best business opportunities in B2B e-Marketplaces. But various ways to specify products and the large variety of e-catalogue formats used by different business actors makes it difficult. This Ph.D. thesis aims to discover potential syntactic and semantic rela-tionships among product data in procurement documents and exploit it to find similar e-catalogues. Using a Concept-based Vector Space Model, product data and its semantic interpretation is used to find the correlation of product data. In order to identify important terms in procurement documents, standard e-catalogues and e-tenders are used as a resource to train a Product Named Entity Recognizer to find B2B product mentions in e-catalogues. The proposed approach makes it possible to use the benefits of all availa-ble semantic resources and schemas but not to be dependent on any specific as-sumption. The solution can serve as a B2B product search system in e-Procurement platforms and e-Marketplaces