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Collaborative filtering (CF) is a personalization technology that generates recommendations for users based on others' evaluations. CF is used by numerous e-commerce Web sites for providing personalized recommendations. Although much research has focused on refining collaborative filtering algorithms, little is known about the effects of user and domain characteristics on the accuracy of collaborative filtering systems. In this study, the effects of two factors—product domain and users' search m

Using a database of 11,001 unique sponsored search keywords, we investigate the relationship between the characteristics of keywords oriented around deal-seeking and brand-seeking and consumer search behaviors and buying propensities. On the basis of the search depth versus search breadth framework, we hypothesize that deal-seeking keywords elicit a search of greater breadth, whereas brand-seeking keywords induce a search of greater depth. We also explore the moderating effect of product type (s

People use social media to help them solve problems and enhance performance. Users may engage in diverse social interactions online, increasing their knowledge base through both weak and strong ties. In this study we investigate the impact of social networks on job performance, focusing on knowledge quality and diversity based on social network theory and the knowledge-based view. The empirical analysis reveals that the strength of social network ties is strongly associated with knowledge qualit

In todayís networked business environment, with the endless increase in available information, relevant information is becoming more and more difficult to find. Collaborative filtering (CF) generates recommendations for users based on othersí evaluations. CF has great potential to improve information search and knowledge reuse. Previous studies have mostly focused on the improvement of CF algorithms. Little research has been done on the effect of users and types of product domains on the per

As the number of business process reengineering (BPR) projects increase in industry, there seems to be a large potential to reduce cost and increase quality through the use of software tools. Currently, more than 50 BPR tools are available, but there has been little research to determine which features of BPR tools are important for BPR success and how important BPR tools are in BPR projects. Research in the related area of tools for computer-aided software engineering (CASE) shows that software

We are moving towards an economy where competitive advantage will be determined by knowledge. In their knowledge management strategies, many companies currently aim to encourage knowledge reuse. In this paper, we examine insights drawn from a related field — software reuse — for their relevance to the emerging field of knowledge reuse. We first examine different types of reuse: components, patterns, frameworks and general principles. We then evaluate different kinds of reuse activities. Fina

In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations wi