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Almost all studies on course recommenders in online platforms target closed online platforms that belong to a University or other provider. Recently, a demand has developed that targets open platforms. Such platforms lack rich user profiles with content metadata. Instead they log user interactions. We report on how user interactions and activities tracked in open online learning platforms may generate...
With an ever-increasing amount of information made available via the Internet, it is getting more and more difficult to find the relevant pieces of information. Recommender systems have thus become an essential part of information technology. Although a lot of research has been devoted to this area, the factors influencing the quality of recommendations are not completely understood. This paper examines...
In this research, we propose using time context to improve predictive accuracy and quality of collaborative filtering for music recommendation. We use time contextual information called micro-profiling. Thus, each user has multiple micro profiles, in particular, six-time slots instead of a single profile. The recommendation is performed depended on these micro-profiling. Our method takes into account...
“Collaborative filtering” (CF) methods provide a good solution for recommendation systems. Neighborhood formation is considered as the main phase in memory approaches. Unfortunately, this phase encounters many problems such as sparsity and scalability, especially for huge datasets which consists of a large number of users and items. This paper presents a new hybrid approach for collaborative filtering...
Recommender systems are designed in such a way that they sort through massive amounts of data so as to help users in finding their preferred items. Currently much research on recommender systems focus on improving the prediction or classification accuracy of the respective algorithms while behavioral aspects are often overlooked. In this paper we focus on a particular behavioral property called monotonicity...
the present study utilizes social computing techniques to enhance the content-based recommender systems. Coined as Enhanced Content-based Algorithm using Social Networking (ECSN), this recommender algorithm is applied in academic social networks to suggest the most relevant items to members of these online societies. In addition to considering user's own preferences, ECSN takes advantage of the interest...
The accuracy of recommendation trends to be worse towards the long tail of the popularity distribution of items, but items in the long tail are generally considered to be valuable as they occupy a majority part of entire data. In this paper, we develop an instance-level cost-sensitive Factorization Machine (FM) to tackle the problem. The new algorithm allows the FM model to automatically leverage...
Recommender systems are the software or technical tools that help user to find out items/things according to his/her preferences from a wide range of items/things. For example, selecting a movie from a large database of movies from on-line or selecting a song of his/her own kind from a large number of songs available in the internet and much more. In order to generate recommendations for the users...
Collaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. As one of the most common approach to recommender systems, CF has been proved to be effective for solving the information overload problem. CF can be divided into two main branches: memory-based and model-based. Most of the present researches improve the accuracy of Memory-based algorithms...
Traditional recommender systems use collaborative filtering or content-based methods to recommend new items for users. New users and items are continuously updated to the system bringing changes in user's preferences, as well as the additional context in form of temporal information. The continuous system updates change not just individual user's preferences, but also group user's preferences affecting...
Collaborative filtering (CF) is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it suffers from several inherent deficiencies such as data sparsity and cold start. To better show user preferences for the cold users additional information (e.g., trust) is often applied. We describe the stages based on which the ratings of an active...
Recommender system is a solution to the information overload problem in websites that allow users to express their interests about items. Collaborative filtering is one of the most important methods in recommender systems which predicts ratings for active user based on opinions and interests of other users who are similar to the active user. Accuracy of ratings prediction can be considerably improved...
Recommendation can be reduced to a sub-problem of link prediction, with specific nodes (users and items) and links (similar relations among users/items, and interactions between users and items). However, the previous link prediction algorithms need to be modified to suit the recommendation cases since they do not consider the separation of these two fundamental relations: similar or dissimilar and...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivity concepts. By propagating "similarity" between users, in a similar way as with "trust", we can significantly expand the space of potential recommenders and system's coverage, improving also the recommendations' accuracy. While "trust" information might be missing or be...
Collaborative Filtering (CF) is one of the most effective technologies in making rating prediction for recommender systems. Traditional user-based CF methods take user-item matrices as input and compute the prediction value based on user similarity. The computation of similarity between each user pair requires exact matching on item set by finding a minimum number of same items rated by both users...
Nowadays, recommender systems occupy an increasingly important position in people's lives. Recommender systems are widely applied in e-commerce websites, they discover users' potential consuming habits by analyzing their behaviors, and then recommend users with what they may purchase. However, recommender systems on e-commerce sites are facing the problem of data sparsity. Data sparsity may cause...
The proliferation of powerful smart devices is revolutionizing mobile computing systems. A particular set of applications that is gaining wide interest is recommender systems. Recommender systems provide their users with recommendations on variety of personal and relevant items or activities. They can play a significant role in today's life whether in E-commerce or for daily decisions that we need...
The Slope One Predictor is suitable for predicting the online rating-base collaborative filtering which is used for analyzing data related to persons' likes or interests in the menu which are variously diverse and the menus are plenty. The system is considered a Personalized Recommender System by using collaborative filtering of satisfaction that the researchers consider the menu fit for the data...
Collaborative filtering(CF) recommender systems are among the most popular approaches to solving the information overload problem in social networks by generating accurate predictions based on the ratings of similar users. Traditional CF recommenders suffer from lack of scalability while decentralized CF recommenders (DHT-based, Gossip-based etc.) have promised to alleviate this problem. Thus, in...
Recommender Systems suggest to users items that may be of interest to them. Collaborative filtering recommender systems suggest the items based on the item ratings provided by similar users in the network. Trust-based recommender systems utilize an explicitly issued trust between users to increase the accuracy of the recommendations. In this paper, we propose a bio-inspired algorithm, called Trust-based...
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