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

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition

Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics.Comment: Accepted to the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019. Contains supplementary material. arXiv admin note: text overlap with arXiv:1804.0383

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Evaluating whether a change in organisational structure would improve its competitive advantage

The purpose of this research is to study and analyse the internal and external structure of Ultimate Clean ltd, where I do work. We have put concentration on background of the company in the starting. This information is followed by aim and scope of research, which shows that what is the research question and what is scope of our research. After that Literature review is elaborated under five main subheadings. These subheading gives us deep information about the literature of organisation structure, competitive advantage. After that, Organisational context with internal and external analyse of the company is given which highlight the strengths, weaknesses, threats and opportunities of the company. Some external factors like political, economic, social and legal, are also discussed in this report. Then some information is given for method of research that why we use it, where and when it is used. Some limitations are also discussed in this report of method. After this, result section comes. In this section, we discussed deeply about the answers of customers, employees and employer. We prepare a discussion of the result and conclude it wisely. In the end, some recommendations are also given to improve organisational structure of Ultimate Clean ltd. We suggest a new structure for the organisation to develop within company to have a good competitive advantage in market place. A big list of references is also given in the end of this report