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Search Results: 1 - 10 of 2664 matches for " Collaborative Filtering "
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Proposing a New Metric for Collaborative Filtering  [PDF]
Arash Bahrehmand, Reza Rafeh
Journal of Software Engineering and Applications (JSEA) , 2011, DOI: 10.4236/jsea.2011.47047
Abstract: The aim of a recommender system is filtering the enormous quantity of information to obtain useful information based on the user’s interest. Collaborative filtering is a technique which improves the efficiency of recommendation systems by considering the similarity between users. The similarity is based on the given rating to data by similar users. However, user’s interest may change over time. In this paper we propose an adaptive metric which considers the time in measuring the similarity of users. The experimental results show that our approach is more accurate than the traditional collaborative filtering algorithm.
A Contextual Item-Based Collaborative Filtering Technology  [PDF]
Xueqing Tan, Pan Pan
Intelligent Information Management (IIM) , 2012, DOI: 10.4236/iim.2012.43013
Abstract: This paper proposes a contextual item-based collaborative filtering technology, which is based on the traditional item-based collaborative filtering technology. In the process of the recommendation, user’s important mobile contextual information are taken into account, and the technology combines with those ratings on the items in the users’ historical contextual information who are familiar with user’s current context information in order to predict that which items will be preferred by user in his or her current context. At the end, an experiment is used to prove that the technology proposed in this paper can predict user’s preference in his or her mobile environment more accurately.
Personalized Tag Recommendation Based on Transfer Matrix and Collaborative Filtering  [PDF]
Shaowu Zhang, Yanyan Ge
Journal of Computer and Communications (JCC) , 2015, DOI: 10.4236/jcc.2015.39002
Abstract: In social tagging systems, users are allowed to label resources with tags, and thus the system builds a personalized tag vocabulary for every user based on their distinct preferences. In order to make the best of the personalized characteristic of users’ tagging behavior, firstly the transfer matrix is used in this paper, and the tag distributions of query resources are mapped to users’ query before the recommendation. Meanwhile, we find that only considering the user’s preference model, the method cannot recommend new tags for users. So we utilize the thought of collaborative filtering, and produce the recommend tags based on the query user and his/her nearest neighbors' preference models. The experiments conducted on the Delicious corpus show that our method combining transfer matrix with collaborative filtering produces better recommendation results.
A Personalized Recommendation Algorithm Based on Associative Sets  [PDF]
Guorui JIANG, Hai QING, Tiyun HUANG
Journal of Service Science and Management (JSSM) , 2009, DOI: 10.4236/jssm.2009.24048
Abstract: During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users’ real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to solve this problem. It uses frequent item sets to filter out noise data, and makes recommendations according to users’ real preferences, so as to enhance the accuracy of recommending results. Test results have proved the superiority of this algorithm.
Improving Recommender Systems in E-Commerce Using Similar Goods  [PDF]
Majid Khalaji, Keramat Mansouri, S. Javad Mirabedini
Journal of Software Engineering and Applications (JSEA) , 2012, DOI: 10.4236/jsea.2012.52015
Abstract: Due to developments of information technology, most of companies and E-shops are looking for selling their products by the Web. These companies increasingly try to sell products and promote their selling strategies by personalization. In this paper, we try to design a Recommender System using association of complementary and similarity among goods and commodities and offer the best goods based on personal needs and interests. We will use ontology that can calculate the degree of complementary, the set of complementary products and the similarity, and then offer them to users. In this paper, we identify two algorithms, CSPAPT and CSPOPT. They have offered better results in comparison with the algorithm of rules; also they don’t have cool start and scalable problems in Recommender Systems.
Illegal Access Detection in the Cloud Computing Environment  [PDF]
Rasim Alguliev, Fargana Abdullaeva
Journal of Information Security (JIS) , 2014, DOI: 10.4236/jis.2014.52007
Abstract:
In this paper detection method for the illegal access to the cloud infrastructure is proposed. Detection process is based on the collaborative filtering algorithm constructed on the cloud model. Here, first of all, the normal behavior of the user is formed in the shape of a cloud model, then these models are compared with each other by using the cosine similarity method and by applying the collaborative filtering method the deviations from the normal behavior are evaluated. If the deviation value is above than the threshold, the user who gained access to the system is evaluated as illegal, otherwise he is evaluated as a real user.
Research on Parameter Optimization in Collaborative Filtering Algorithm  [PDF]
Zijiang Zhu
Communications and Network (CN) , 2018, DOI: 10.4236/cn.2018.103009
Abstract: Collaborative filtering algorithm is the most widely used and recommended algorithm in major e-commerce recommendation systems nowadays. Concerning the problems such as poor adaptability and cold start of traditional collaborative filtering algorithms, this paper is going to come up with improvements and construct a hybrid collaborative filtering algorithm model which will possess excellent scalability. Meanwhile, this paper will also optimize the process based on the parameter selection of genetic algorithm and demonstrate its pseudocode reference so as to provide new ideas and methods for the study of parameter combination optimization in hybrid collaborative filtering algorithm.
A Multi -Perspective Evaluation of MA and GA for Collaborative Filtering Recommender System
Hema Banati,Shikha Mehta
International Journal of Computer Science & Information Technology , 2010,
Abstract: The rising popularity of evolutionary algorithms to solve complex problems has inspired researchers toexplore their utility in recommender systems. Recommender systems are intelligent web applications whichgenerate recommendations keeping in view the user’s stated and unstated requirements. Evolutionaryapproaches like Genetic and memetic algorithms have been considered as one of the most successfulapproaches for combinatorial optimization. Memetic Algorithms (MAs) are enhanced genetic algorithmswhich incorporate local search in the evolutionary scheme. Local Search process on each solution afterevery generation helps in improving the convergence time of MA. This paper presents multi-perspectivecomparative evaluation of memetic and genetic evolutionary algorithms for model based collaborativefiltering recommender system. Experimental study was conducted on MovieLens dataset to investigate thedecision support and statistical efficiency of Memetic and genetic algorithms. Algorithms were analyzedfrom different perspectives like variation in number of clusters, effect of increasing the number of users,varying number of recommendations and using either one or more than one cluster for computing ratingsof the unrated items. Results obtained demonstrated that from all perspectives memetic collaborativefiltering algorithm has better predictive accuracy as compared genetic collaborative filtering algorithm.
Assement of the Seismic Behavior of Eccentrically Braced frame with Vertical and Horizontal Link Made of Easy-Going Steel and Constructional Steel
Yaser Mozaffary Jouybary,Abbas Akbarpour Nikghalb
Research Journal of Applied Sciences, Engineering and Technology , 2012,
Abstract: This study discussed about Assement of the seismic behavior of eccentrically braced frame. The variety of social networks and virtual communities has created problematic for users of different ages and preferences; in addition, since the true nature of groups is not clearly outlined, users are uncertain about joining various virtual groups and usually face the trouble of joining the undesired ones. As a solution, in this paper, we introduced the hybrid community recommender system which offers customized recommendations based on user preferences. Although techniques such as content based filtering and collaborative filtering methods are available, these techniques are not enough efficient and in some cases make problems and bring limitations to users. Our method is based on a combination of content based filtering and collaborative filtering methods. It is created by selecting related features of users based on supervised entropy as well as using association rules and classification method. Supposing users in each community or group share similar characteristics, by hierarchical clustering, heterogeneous members are identified and removed. Unlike other methods, this is also applicable for users who have just joined the social network where they do not have any connections or group memberships. In such situations, this method could still offer recommendations.
Hybrid Recommender System for Joining Virtual Communities
Leila Esmaeili,Behrouz Minaei-Bidgoli,Hamid Alinejad-Rokny,Mahdi Nasiri
Research Journal of Applied Sciences, Engineering and Technology , 2012,
Abstract: The variety of social networks and virtual communities has created problematic for users of different ages and preferences; in addition, since the true nature of groups is not clearly outlined, users are uncertain about joining various virtual groups and usually face the trouble of joining the undesired ones. As a solution, in this study, we introduced the hybrid community recommender system which offers customized recommendations based on user preferences. Although techniques such as content based filtering and collaborative filtering methods are available, these techniques are not enough efficient and in some cases make problems and bring limitations to users. Our method is based on a combination of content based filtering and collaborative filtering methods. It is created by selecting related features of users based on supervised entropy as well as using association rules and classification method. Supposing users in each community or group share similar characteristics, by hierarchical clustering, heterogeneous members are identified and removed. Unlike other methods, this is also applicable for users who have just joined the social network where they do not have any connections or group memberships. In such situations, this method could still offer recommendations.
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