An environment for protecting the privacy of e-shoppers

Privacy, an everyday topic with weekly media coverage of loss of personal records, faces its bigger risk during the uncontrolled, involuntary or inadvertent disclosure and collection of personal and sensitive information. Preserving one's privacy while e-shopping, especially when personalisation is involved, is a big challenge. Current initiatives only offer customers opt-out options. This research proposes a `privacy-preserved' shopping environment (PPSE) which empowers customers to disclose information safely by facilitating a personalised e- shopping experience that protects their privacy. Evaluation delivered positive results which suggest that such a product would indeed have a market in a world where customers are increasingly concerned about their privacy

Data Mining for Recommendation System in e-Commerce

This is e-commerce to sell wigs and hairpieces. This company has their own off-line business as well. When I started this project they wanted to build not only web-site but also recommendation system. One more request was that they would like to reflect their own business know-how. Now I will show what classic recommendation system is and how I solved their request. In off-line business, they are talking with customer and they can guess what they want and what will fit to them and then they can recommend some item to customer. In e-commerce, it is difficult to apply customer’s request in real-time. What is the most interactive data in web-site? The answer is web log from web server so most of e-commerce use and analyze that for their system. In this paper, we designed database to transfer from off-line knowledge to on-line recommendation system that can help selling product. This paper concern about Collaborative filter and association rule mining which is popular in this field. In this site, we need special database design to solve their request, to add their own experience and data preparation. This paper shows how data mining can help e-business to improve their customer relationship and make intelligent business strategies

A recommender system for Pingo Doce & Go Nova

Using the Design Science framework, and acknowledging the success of recommenders in e-commerce settings, this paper proposes the design and implementation of a recommender in a physical retail store(Pingo Doce & Go Nova). It allows to assess if the recommender can influence customers’ decisions, increase sales, the number of unique products acquired, and understanding the customers. To develop it, the data was collected, curated, recommendation strategies were designed (loyalty, novelty, and related) and the customers were split into groups. The recommender will be deployed in the storeapp and, after, the results from the metrics will be analyzed

An Efficient Algorithm for Frequent Pattern Mining for Real-Time Business Intelligence Analytics in Dense Datasets

Finding frequent patterns from databases has been the most time consuming process in data mining tasks, like association rule mining. Frequent pattern mining in real-time is of increasing thrust in many business applications such as e-commerce, recommender systems, and supply-chain management and group decision support systems, to name a few. A plethora of efficient algorithms have been proposed till date, among which, vertical mining algorithms have been found to be very effective, usually outperforming the horizontal ones. However, with dense datasets, the performances of these algorithms significantly degrade. Moreover, these algorithms are not suited to respond to the real-time need. In this paper, we describe BDFS(b)-diff-sets, an algorithm to perform real-time frequent pattern mining using diff-sets and limited computing resources. Empirical evaluations show that our algorithm can make a fair estimation of the probable frequent patterns and reaches some of the longest frequent patterns much faster than the existing algorithms.

Cost-oriented recommendation model for e-commerce

Contemporary Web stores offer a wide range of products to e-customers. However, online sales are strongly dominated by a limited number of bestsellers whereas other, less popular or niche products are stored in inventory for a long time. Thus, they contribute to the problem of frozen capital and high inventory costs. To cope with this problem, we propose using information on product cost in a recommender system for a Web store. We discuss the proposed recommendation model, in which two criteria have been included: a predicted degree of meeting customer’s needs by a product and the product cost

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

Light-Weight Digital Receipt System

The sole purpose of this project is to develop a light-weight digital receipt system that can counter the short-comings of the conventional digital receipt system and meet the market expectations desirably. The proposed system mainly focuses on achieving simpler receipt data extraction, leverage the use of server to a third party and eliminate as much cost as possible. The approach proposed is mainly used to counter problems and complications which are found in existing digital system nowadays. The author believes that retrieving receipt data must not necessary be done at the server, but could also be done at the POS system, particularly at the printer point. Besides, the author's proposed digital receipt will not require any reconfiguration processes to be done at retailer's POS system, which retailers will not need to worry for their POS system being modified just to integrate with digital receipt applications. The author will strive to produce a digital receipt system that is very cost-effective as compared to the conventional digital receipt system so that both retailer and customer can afford to use such as technology

Behavior-based personalization : strategies and Implications

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 53-55).The personalization of services and products offered to customers is becoming crucial for the success of companies. Firms that can maintain a personalized relationship with their customers will not only gain an advantage from competitors but will also benefit from having more loyal and valuable customers. The recent advances in technology and the associated cost reduction are allowing companies to gather information about their customers and their behavior in an easy and inexpensive way. This collection and analysis of behavior-based information increases the companies' knowledge about their customers and allows a more personalized approach. This thesis studies what has been accomplished in the domain of behavior-based personalization and in more detail what are the techniques and strategies being used and how companies can take advantage of its applications. Moreover, this thesis discusses the critical role of personalization in building effective customer relationships management (CRM) strategies.by João G. Violante.S.M

The use of XML schema and XSLT rules for product information personalization

This thesis describes research carried out in order to help solve the problem of personalization in e-commerce/CRM system. Web-based personalization consists of activities, such as providing customised information, that tailor the user's Web experience- browsing a Web site or purchasing a product, for example-to that user's particular needs. The main research objective of the project is to investigate how XSLT technologies can be used for the development of matching engines that find XML represented products that match the tastes, needs or requirements of customers as captured in customer profiles, also represented in XML. More specifically our research investigates into novel algorithms for transforming XML based product specifications using rules that derive from mining customer profiles with the purpose of customizing the product information