A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Res...

Recommender systems attract growing attention recently, as they can suggest appropriate choices to users based on intelligent prediction. As one of the most popular recommender system techniques, Collaborative Filtering achieves efficiency from the similarity measurement of users and items. However, existing similarity measurement methods have reduced accuracy due to data correlation and sparsi...

Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge – either hand-coded knowledge provided by experts or “mined” knowledge learned from the behavior of consumers – to guide consumers through the often-overwhelmi...

In this article we present an explanation of how recommender systems are related to some traditional database analysis techniques. We examine how recommender systems help E-commerce sites increase sales and analyze the recommender systems at six market-leading sites. Based on these examples, we create a taxonomy of recommender systems, including the inputs required from the consumers, the addit...

The purpose if this master’s thesis is to study and develop a new algorithmic framework for Collaboartive Filtering to produce recommendations in the top-N recommendation problem. Thus, we propose Lanczos Latent Factor Recommender (LLFR); a novel “big data friendly” collaborative filtering algorithm for top-N recommendation. Using a computationally efficient Lanczos-based procedure, LLFR builds...

In this paper we describe a case study where researchers in the social sciences (n=19) assess topical relevance for controlled search terms, journal names and author names which have been compiled by recommender services. We call these services Search Term Recommender (STR), Journal Name Recommender (JNR) and Author Name Recommender (ANR) in this paper. The researchers in our study (practitione...

Definitions Recommendation generation problem. Given a set of users and their (incomplete) preferences over set of items, find, for each user new items for which they would have high preferences. Typically, utility function is incomplete. Problem. The main problem solved by collaborative filtering methods/recommender systems can be phrased in a number of ways: • User-based recommendations. Give...

recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. user similarity measurement plays an important role in collaborative filtering based recommender systems. in order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...

Continuous processing of top-k queries over data streams is a promising technique for alleviating the information overload problem as it distinguishes relevant from irrelevant data stream objects with respect to a given scoring function over time. Thus it enables filtering of irrelevant data objects and delivery of top-k objects relevant to user interests in real-time. We propose a solution for...

Recommender systems have frequently been evaluated with respect to their average performance for all users. However, optimizing such recommender systems regarding those evaluation measures might provide worse results for a subset of users. Defining a difficulty measure allows us to evaluate and optimize recommender systems in a personalized fashion. We introduce an experimental setup to evaluat...