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Similarity measure and instance selection for collaborative filtering

Chun Zeng, Chun-Xiao Xing, Li-Zhu Zhou
2003 Proceedings of the twelfth international conference on World Wide Web - WWW '03  
We adopt two techniques: a matrix conversion method for similarity measure and an instance selection method.  ...  Collaborative filtering has been very successful in both research and applications such as information filtering and E-commerce.  ...  The next section provides a brief background in collaborative filtering algorithms. Section 3 discusses our method for similarity measure.  ... 
doi:10.1145/775152.775243 dblp:conf/www/ZengXZ03 fatcat:krwnszkrb5ftjh3nqlpa4453be

Selecting relevant instances for efficient and accurate collaborative filtering

Kai Yu, Xiaowei Xu, Martin Ester, Hans-Peter Kriegel
2001 Proceedings of the tenth international conference on Information and knowledge management - CIKM'01  
We introduce an information theoretic approach to measure the relevance of a consumer (instance) for predicting the preference for the given product (target concept).  ...  Collaborative filtering uses a database about consumers' preferences to make personal product recommendations and is achieving widespread success in both E-Commerce and Information Filtering Applications  ...  In the following sections, we will study the "quality" of the instances and propose to select the instances of high "quality" for faster and more accurate collaborative filtering.  ... 
doi:10.1145/502624.502626 fatcat:likqbdge7zfhvbfof5wfet67cq

Selecting relevant instances for efficient and accurate collaborative filtering

Kai Yu, Xiaowei Xu, Martin Ester, Hans-Peter Kriegel
2001 Proceedings of the tenth international conference on Information and knowledge management - CIKM'01  
We introduce an information theoretic approach to measure the relevance of a consumer (instance) for predicting the preference for the given product (target concept).  ...  Collaborative filtering uses a database about consumers' preferences to make personal product recommendations and is achieving widespread success in both E-Commerce and Information Filtering Applications  ...  In the following sections, we will study the "quality" of the instances and propose to select the instances of high "quality" for faster and more accurate collaborative filtering.  ... 
doi:10.1145/502585.502626 dblp:conf/cikm/YuXEK01 fatcat:uyjubupwc5aehok2dhaelf4rfm

Collaborative Filtering with Semantic Neighbour Discovery [chapter]

Bruno Veloso, Benedita Malheiro, Juan C. Burguillo
2016 Lecture Notes in Computer Science  
Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences.  ...  time for the predicted recommendations.  ...  within project «POCI-01-0145-FEDER-006961» and by National Funds through the FCT -Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014  ... 
doi:10.1007/978-3-319-47955-2_23 fatcat:lznfab3qlfdgpls54wr2nncb4i

Relational distance-based collaborative filtering

Wei Zhang
2008 Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08  
In this paper, we present a novel hybrid recommender system called RelationalCF, which integrate content and demographic information into a collaborative filtering framework by using relational distance  ...  computation approaches without the effort of form transformation and feature construction.  ...  In our current experiments, we use RIBL distances measure for distances between sets of objects and use alignment-based edit distance measure for distances between for lists of objects.  ... 
doi:10.1145/1390334.1390551 dblp:conf/sigir/Zhang08 fatcat:2gt6vbgyszh5vjgdo7txg3hx4q

A hybrid approach with collaborative filtering for recommender systems

Gilbert Badaro, Hazem Hajj, Wassim El-Hajj, Lama Nachman
2013 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC)  
We introduce a hybrid approach for solving the problem of finding the ratings of unrated items in a user-item ranking matrix through a weighted combination of user-based and itembased collaborative filtering  ...  The evaluation of the system shows superiority of the solution compared to stand-alone user-based collaborative filtering or item-based collaborative filtering.  ...  The highest N similarity values are selected for each technique, i.e., the user-user similarity measure and the item-item similarity measure.  ... 
doi:10.1109/iwcmc.2013.6583584 dblp:conf/iwcmc/BadaroHEN13 fatcat:ac5q6mqg5zaetmli2z2zc2qf3i

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

G. Parthasarathy, S. Sathiya Devi
2022 Computers Materials & Continua  
This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations.  ...  In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure.  ...  Further, the class based collaborative filtering algorithm was described by Zeng et al. [18] which adapts the user frequency threshold methodology for instance selection.  ... 
doi:10.32604/cmc.2022.019805 fatcat:yuhoivk6rnacfkomoj4cdikeuy

Sense-Share: A Framework for Resilient Collaborative Service Performance Monitoring

Khulan Batbayar, Emmanouil Dimogerontakis, Roc Meseguer, Leandro Navarro, Ramin Sadre
2019 2019 15th International Conference on Network and Service Management (CNSM)  
We propose Sense-Share, a simple light-weight and resilient collaborative sensing framework based on the similarity of the client nodes' perception of service performance.  ...  Different techniques are used to provide performance monitoring information so that client nodes can select the best service instance.  ...  First, we study how the trust-based filtering component can filter the less similar collaborators.  ... 
doi:10.23919/cnsm46954.2019.9012683 dblp:conf/cnsm/BatbayarDMNS19 fatcat:wmzfe2hl45aptnx36fr7hl56qi

Alors: An algorithm recommender system

Mustafa Mısır, Michèle Sebag
2017 Artificial Intelligence  
Two merits of collaborative filtering (CF) compared to the mainstream algorithm selection (AS) approaches are the following.  ...  Algorithm selection (AS), selecting the algorithm best suited for a particular problem instance, is acknowledged to be a key issue to make the best out of algorithm portfolios.  ...  for their many critiques and suggestions that helped considerably to improve the paper.  ... 
doi:10.1016/j.artint.2016.12.001 fatcat:d63uj7l5srg53k4odt36fb7gxm

A Study on Recommendation Systems in Location Based Social Networking

Lakshmi Shree Kullappa, Rajeshwari Kullappa
2017 Journal of Information and Organizational Sciences  
This paper presents a comprehensive survey of recommended systems for LBSNs covering the concepts of LBSNs, terminologies of LBSN and various recommendation systems.  ...  The resulting information can be used to market a product and to improve business, as well recommend a travel and plan an itinerary.  ...  The two variants of Collaborative filtering are proposed, Friend-based Collaborative Filtering (FCF) and Geo-Measured Friend-based Collaborative Filtering (GM-FCF).  ... 
doi:10.31341/jios.41.2.6 fatcat:dgaterc7e5dnfip6vge3ag44hy

Estimating Trust Strength For Supporting Effective Recommendation Services

Chih-Ping Wei, Hung-Chen Chen, Ming-Kai Liang
2011 Pacific Asia Conference on Information Systems  
Our empirical evaluation results show that our proposed approach outperforms our benchmark techniques, i.e., the traditional collaborative filtering approach and the original trustbased one.  ...  In addition to the well-known collaborative filtering recommendation approach, the trust-based recommendation approach is the emerging one.  ...  In practice, we maintain all disappeared instances and randomly select survival instances for each bag to keep them balanced.  ... 
dblp:conf/pacis/WeiCL11 fatcat:d5svsvjqvrajlfa4bonh3vqnke

Personal Recommender System Based on Agglomerative Clustering together with User-based and Item-based Collaborative Filtering Methods

Ratawan Phantunin, School of Information Technology, Sripatum University, Bangkok, Thailand., N. Chirawichitchai
2020 Journal of Software  
Collaborative Filtering or Item-based Collaborative Filtering alone.  ...  collaborative filtering.  ...  The collaborative filtering is a highly successful and popular technique for developing the recommender systems [2] for instance, Movie Recommender System, since users are not able to study the details  ... 
doi:10.17706/jsw.15.6.163-171 fatcat:p3bpr4jpnrbr7pbynpp3ft4bda

Removing redundancy and inconsistency in memory-based collaborative filtering

Kai Yu, Xiaowei Xu, Anton Schwaighofer, Volker Tresp, Hans-Peter Kriegel
2002 Proceedings of the eleventh international conference on Information and knowledge management - CIKM '02  
The application range of memory-based collaborative filtering (CF) is limited due to CF's high memory consumption and long runtime.  ...  In our approach, we consider instance selection as the problem of selecting informative data that increase the a posteriori probability of the optimal model.  ...  Instance Selection for Memory-Based CF We now turn our attention to collaborative filtering (CF) for recommender systems.  ... 
doi:10.1145/584800.584804 fatcat:cttcl5m5o5ao7ilvyfmrwjw4uu

Removing redundancy and inconsistency in memory-based collaborative filtering

Kai Yu, Xiaowei Xu, Anton Schwaighofer, Volker Tresp, Hans-Peter Kriegel
2002 Proceedings of the eleventh international conference on Information and knowledge management - CIKM '02  
The application range of memory-based collaborative filtering (CF) is limited due to CF's high memory consumption and long runtime.  ...  In our approach, we consider instance selection as the problem of selecting informative data that increase the a posteriori probability of the optimal model.  ...  Instance Selection for Memory-Based CF We now turn our attention to collaborative filtering (CF) for recommender systems.  ... 
doi:10.1145/584792.584804 dblp:conf/cikm/YuXSTK02 fatcat:exdnoyt5pjcnve6tkq2emrufxi

Improving Neighborhood-Based Collaborative Filtering by a Heuristic Approach and an Adjusted Similarity Measure

Yasser El Madani El Alami, El Habib Nfaoui, Omar El Beqqali
2015 International Conference on Big Data Cloud and Applications  
In addition, we employ an adjusted similarity measure that combines Pearson correlation with a set-similarity measure (such as Jaccard similarity) as a correction coefficient for .accurate similarities  ...  Collaborative filtering" is the most used approach in recommendation systems since it provides good predictions.  ...  One of the major factors in collaborative filtering that greatly influences the recommendation accuracy is the selected similarity measure.  ... 
dblp:conf/bdca/AlamiNB15 fatcat:i5pob2cc2rdtrbuxu57eawlxhq
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