Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations based on explicit ratings on items from other users. However, in some ecommerce environments such as a mobile environment, it is difficult to collect explicit feedback data; only implicit feedback is available. In this paper,...

A plethora of collaborative filtering algorithms have been proposed in related literature. Due to the dynamic and changing parameters of the various application contexts, careful testing and parameterization has to be carried out before an algorithm is finally deployed in a real setting. This paper investigates how a previously proposed tool for simulated testing of collaborative filtering algo...

In modern business environment, product life cycle gets shorter and the customer’s buying preference changes over time. Time plays a more and more important role in collaborative filtering. However, there is a gap in one class collaborative filtering (OCCF). On the basis of collecting different real-time information, this paper proposes an optimization model for e-retailers. Through comparing d...

We investigate the impact of several different recommender algorithms (e.g., Amazon.com's “Consumers who bought this item also bought”), commonly used in ecommerce and online services, on sales volume and diversity, using field experiment data on movie sales from a top retailer in North America. Sales volume refers to the number of products purchased or amount of money spent by individual consu...

Collaborative filtering (CF) techniques have proved to be effective in their application to e-commerce and other application domains. However, their applicability to the recommendation of learning resources deserve separate attention as seeking learning resources can be hypothesized to be substantially different from selecting information resources or products for purchase. To date there are on...

Electronic Commerce has becomes an important means for tourism enterprises to face the increasing competition. How to provide the personalized service for customers is an important issue to raise the service level of tourism. Through analyzing characteristics of tourism, a Personalized Recommendation Model is proposed at the basis of user’s rating feature. It has following features: (1) Pre-pro...

In this article, a novel CF (collaborative filtering)-based recommender system is developed for e-commerce sites. Unlike the conventional approach in which only binary purchase data are used, the proposed approach analyzes the data captured from the navigational and behavioral patterns of customers, estimates the preference levels of a customer for the products which are clicked but not purchas...

Recommender system has become an indispensable component in many e-commerce sites. One major challenge that largely remains open is the coldstart problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RAPARE) to break this ice barrier. The center-piece of our RAPARE is to provide a fi...

Most of the software agents only perform simple product price comparisons; some support the purchase of products or the negotiation over multiple terms of a transaction, such as, e. g., warranties, return policies, delivery times and loan options. Auctions help to find an effective pricing mechanism in electronic commerce. The active technologies enabling customers to purchase efficiently force...

Knowledge sharing and transfer are essential for learning in groups, especially when group members have different disciplinary expertise and collaborate online. ComputerSupported Collaborative Learning (CSCL) environments have been designed to facilitate transactive knowledge sharing and transfer in collaborative problem-solving settings. This study investigates how knowledge sharing and transf...