Related papers: Positive emotions help rank negative reviews in e-…
In this paper, we present our work towards comparing on-line and off-line evaluation metrics in the context of small e-commerce recommender systems. Recommending on small e-commerce enterprises is rather challenging due to the lower volume…
E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations' influence on customer clicks and buys, three target areas -- customer…
In electronic marketplaces, after each transaction buyers will rate the products provided by the sellers. To decide the most trustworthy sellers to transact with, buyers rely on trust models to leverage these ratings to evaluate the…
A key aspect of word of mouth marketing are emotions. Emotions in texts help propagating messages in conventional advertising. In word of mouth scenarios, emotions help to engage consumers and incite to propagate the message further. While…
Online marketplaces, search engines, and databases employ aggregated social information to rank their content for users. Two ranking heuristics commonly implemented to order the available options are the average review score and item…
A massive amount of reviews are generated daily from various platforms. It is impossible for people to read through tons of reviews and to obtain useful information. Automatic summarizing customer reviews thus is important for identifying…
Online commerce relies heavily on user generated reviews to provide unbiased information about products that they have not physically seen. The importance of reviews has attracted multiple exploitative online behaviours and requires methods…
User reviews on e-commerce and review sites are crucial for making purchase decisions, although creating detailed reviews is time-consuming and labor-intensive. In this study, we propose a novel use of dialogue systems to facilitate user…
Characterizing the reputation of an evaluator is particularly significant for consumer to obtain useful information from online rating systems. Furthermore, to overcome the difficulties with spam attacks on the rating system and to get the…
Due to flourish of the Web 2.0, web opinion sources are rapidly emerging containing precious information useful for both customers and manufactures. Recently, feature based opinion mining techniques are gaining momentum in which customer…
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.…
Agent mediated e-commerce involves buying and selling on Internet through software agents. The success of an agent mediated e-commerce system lies in the underlying reputation management system which is used to improve the quality of…
Online reviews are typically written by volunteers and, as a consequence, information about seller quality may be under-provided in digital marketplaces. We study the extent of this under-provision in a large-scale randomized experiment…
The conventional way of summarizing ratings or sentiment of reviews of customers on products of an online shopping brand are not sufficient to evaluate the financial health of that brand. It overlooks the social standing and influence of…
Users on the internet usually require venues to provide better purchasing recommendations. This can be provided by a reputation system that processes ratings to provide recommendations. The rating aggregation process is a main part of…
Reputation systems aim to reduce the risk of loss due to untrustworthy participants. This loss is aggravated by dishonest advisors trying to pollute the e-market environment for their self-interest. A major task of a reputation system is to…
Platforms critically rely on rating systems to learn the quality of market participants. In practice, however, these ratings are often highly inflated, and therefore not very informative. In this paper, we first investigate whether the…
E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming more prevalent and…
Position bias poses a persistent challenge in recommender systems, with much of the existing research focusing on refining ranking relevance and driving user engagement. However, in practical applications, the mitigation of position bias…
We develop a decision making framework to cast the problem of learning a ranking policy for search or recommendation engines in a two-sided e-commerce marketplace as an expected reward optimization problem using observational data. As a…