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As the most widely used recommendation algorithm, collaborative filtering (CF) has been studied for many years due to its simplicity and effectiveness. The two main categories of CF have their own shortcomings. Memory-based CF can't generate accurate results when faced with data sparsity; and model-based CF always loses the information between users or items. To alleviate this problem, we propose...
An important goal of a recommender system is to solve the top-k recommendation problem, however, there is no perfect ranking list for any recommender algorithm. Much work has been done on the recommendation list to improve user experience. In this paper, we focus on the technique of dithering which can be used in an online recommendation situation and be neglected in most academic research, and propose...
Collaborative Filtering (CF) is widely applied to personalized recommendation systems. Traditional collaborative filtering techniques make predictions through a user-item matrix of ratings which explicitly presents user preference. With the increasingly growing number of users and items, insufficient rating data still leads to the decreasing predictive accuracy with traditional collaborative filtering...
Recommendation systems play a critical role in the Information Science application domain, especially in ecommerce ecosystems. In almost all recommender systems, statistical methods and machine learning techniques are used to recommend items to the users. Although the user-based collaborative filtering approaches have been applied successfully in many different domains, some serious challenges remain...
According to the recommendation quality is not high and cold start problem of the recommendation system in the case of sparse data, a collaborative filtering algorithm based on the combination of matrix decomposition technique and social network trust model is proposed. First of all, in the degree of trust computing, expert node method is introduced to determine the existence of multiple paths of...
Online music radios, such as Last.fm and Douban.fm, which provide users with free music, have gained much popularity in recent years. In online music radios, music recommendation plays a central role in recommending the most relevant music to users who are most likely to listen to. Different from traditional on-demand music service, online music radios have only users' listening records instead of...
Finding appropriate adslots to display ads is an important step to achieve high conversion rates in online display advertising. Previous work on ad recommendation and conversion prediction often focuses on matching between adslots, users and ads simultaneously for each impression at micro level. Such methods require rich attributes of users, ads and adslots, which might not always be available, especially...
User-Item matrix (UI matrix) has been widely used in recommendation systems for data representation. However, as the amount of users and items increases, UI matrix becomes very sparse, which leads to unsatisfactory performance in traditional recommendation algorithms. To address this problem, in this paper, a rating prediction method with low sensitivity to sparse datasets is proposed. This method...
As the clustering-based model has better scalability than typical collaborative filtering methods, it has become one of the most successful approaches for recommender systems. However, since clustering-based algorithms often result in nearby users being divided into different clusters, they only recommend items being rated by users belonging to the same cluster with the active user, and recommendation...
The L2 shared cache is an important resource for multi-core system. The cache replacement algorithm of L2 shared cache is one of the key factors in judging whether the L2 shared cache of multi-core system is efficient. In this paper, we study shared-cache simulation for multi-core with the LRU2-MRU collaborative cache replacement algorithm. We propose a theoretical foundation for LRU2-MRU to show...
With the increasing numbers of Web services and service users on World Wide Web, predicting QoS(Quality of Service) for users will greatly aid service selection and discovery. Due to the different backgrounds and experiences of users, they have different QoS experiences when interacting with the same service. Even two users who have similar experiences on some services can have diverging views when...
Data mining meta-optimization aims to find an optimal data mining model which has the best performance (e.g., highest prediction accuracy) for a specific dataset. The optimization process usually involves evaluating a series of configurations of parameter values for many algorithms, which can be very time-consuming. We propose an agent-based framework to power the meta-optimization through collaboration...
Taking into account continuously growing content wealth of pervasive environments generally, user needs assistance to find what he want in short time. Specifically in pervasive learning environment where learners are surrounded by numerous suppliers and the rich resources offered by the learning platform, the personalized recommender systems seems important for providing user by convenience and fulfil...
Collaborative filtering (CF) is one of the most effective types of recommender systems. As data sparsity remains a significant challenge for CF, we consider basing predictions on imputed data, and find this often improves performance on very sparse rating data. In this paper, we propose two imputed neighborhood based collaborative filtering (INCF) algorithms: imputed nearest neighborhood CF (INN-CF)...
The challenges of deriving early-adopter competitive advantage, even with fabless access to process technology, through leveraging features offered by the advanced, and possibly disruptive, process technologies in real SoC products, are outlined. A structured methodology for addressing these challenges, and bridging the gap between process and design, sufficiently early in the development cycle to...
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