We present an item-based approach for collaborative filtering. We determine a list of recommended items for a user by considering their previous purchases. Additionally other features of the users could be considered such as page views, search queries, etc. . . In particular we address the problem of efficiently comparing items. Our algorithm can efficiently approximate an estimate of the simil...

User counterparts, such as user attributes in social networks or interests, are the keys to more natural Human–Computer Interaction (HCI). In addition, users’ and structures help us understand complex interactions HCI. Most previous studies have been based on supervised learning improve performance of However, real world, owing signal malfunctions devices, large amounts abnormal information, un...

The system to Contextual Suggestion Track at TREC2012 includes information crawling and preprocessing, context filtering, user modeling, similarity computing and ranking, description generating. Some third party tool kits are used, such as URLPARSE. TF-IDF (term frequency–inverse document frequency) and cosine similarity is also used for building user models and computed similarities between us...

Recommender system data presents unique challenges to the data mining, machine learning, and algorithms communities. The high missing data rate, in combination with the large scale and high dimensionality that is typical of recommender systems data, requires new tools and methods for efficient data analysis. Here, we address the challenge of evaluating similarity between two users in a recommen...

in recent years, collaborative filtering (cf) methods are important and widely accepted techniques are available for recommender systems. one of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. however, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. with the dev...