In many online applications, the range of content that is offered to users is so wide that a need for automated recommender systems arises. Such systems can provide a personalized selection of relevant items to users. In practice, this can help people find entertaining movies, boost sales through targeted advertisements, or help social network users meet new friends. To generate accurate person...

1. Introduction Recommender systems provide advice to users about items they might wish to purchase or examine. Recommendations made by such systems can help users navigate through large information spaces of product descriptions, news articles or other items. As on-line information and e-commerce burgeon, recommender systems are an increasingly important tool. A recent survey of recommender sy...

Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present ”ItemRank”, a random–walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top...

In the era of Internet, web is a giant source of information. The constantly growing rate of information in the web makes people confused to decide which product is relevant to them. To find relevant product in today’s era is very time consuming and tedious task. Everyday a lot of information is uploaded and retrieved from the web. The web is overloaded with information and it is very essential...

The implementation of recommender systems in electronic procurement processes for service packages, consisting of productand service components requires a consideration of strategic, tactical and operational procurement as well as information and communication technologies in value networks. Increasingly, the design of recommender systems for procurement processes in value networks is of scient...

ABSTRACT Typical recommender evaluations treat users as an homogeneous unit. However, user subgroups often differ in their tastes, which can result more broadly in diverse recommender needs. Thus, these groups may have different degrees of satisfaction with the provided recommendations. We explore the offline top-N performance of collaborative filtering algorithms across two domains. We find th...

Recommender Systems are effective methods that have been developed successfully in e-commerce to provide personal recommendations based on user’s previous preferences. A recommender system assists users in a decision-making process to suggest interesting items from overwhelming flood of data available in the system. Suitably adapting the technology, recommender systems for medical purposes have...

The issue of trust is important in recommender systems. These systems are typically described in terms of perceived reliability of the recommender coupled with a content quality perspective. However, most studies do not address the complete user context and psychological environment of a recommender system. This environment and context are described here by three primary areas of consideration,...

Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practi...

When building a recommender system, how can we ensure that all items are modeled well? Classically, recommender systems are built, optimized, and tuned to improve a global prediction objective, such as root mean squared error. However, as we demonstrate, these recommender systems often leave many items badly-modeled and thus under-served. Further, we give both empirical and theoretical evidence...