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190
Cross-Domain Collaborative Filtering via Bilinear Multilevel Analysis
- PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2013
"... Cross-domain collaborative filtering (CDCF), which aims to leverage data from multiple domains to relieve the data sparsity issue, is becoming an emerging research topic in recent years. However, current CDCF methods that mainly consider user and item factors but largely neglect the heterogeneity of ..."
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Cited by 1 (0 self)
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Cross-domain collaborative filtering (CDCF), which aims to leverage data from multiple domains to relieve the data sparsity issue, is becoming an emerging research topic in recent years. However, current CDCF methods that mainly consider user and item factors but largely neglect the heterogeneity
Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer
"... The Cross Domain Collaborative Filtering (CDCF) exploits the rating matrices from mul-tiple domains to make better recommendations. Existing CDCF methods adopt the sub-structure sharing technique that can only transfer linearly correlated knowledge between domains. In this paper, we propose the noti ..."
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The Cross Domain Collaborative Filtering (CDCF) exploits the rating matrices from mul-tiple domains to make better recommendations. Existing CDCF methods adopt the sub-structure sharing technique that can only transfer linearly correlated knowledge between domains. In this paper, we propose
Personalized Recommendation via Cross-Domain Triadic Factorization
"... Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic ..."
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Cited by 8 (0 self)
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Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research
Transfer learning for collaborative filtering via a rating-matrix generative model
- in Proceedings of the 26th International Conference on Machine Learning
, 2009
"... Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating m ..."
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Cited by 36 (9 self)
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Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating
Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis
, 2003
"... Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic ..."
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Cited by 96 (0 self)
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Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization
1 Cross-Domain Ontology Resolution in Net-Centric Command and Control
"... We have argued that the success of Net-Centric Operations and Warfare (NCOW) depends upon the ability of net-centric environment (NCE) users—both human and automated—to readily discover useful information and Web-based services. Effective discovery requires, in turn, effective meta-data “tagging. ” ..."
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taxonomy evolution using machine learning and intelligent agent technology. In this paper we analyze the underlying reasons for this claim and show that what is really needed is a way to allow multiple ontologies (along with their taxonomic correlates) with cross-domain (i.e., inter-ontology) resolution
User-based collaborative filtering on cross domain by tag transfer learning,”
- in Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining,
, 2012
"... ABSTRACT Exploiting social tag information has been a popular way to improve recommender systems in recent years. However, recommender systems could not be improved with tags when tags are sparse. We notice that, while the tags are sparse for recommendation on some target domains, related and relat ..."
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Cited by 4 (1 self)
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and relatively dense auxiliary tags may already exist in some other more mature application domains. This inspires us to transfer tags to improve recommender systems on cross domain. In this paper, we propose a Tag Transfer Learning (TTL) model for effective cross domain collaborative filtering. TTL has some
Adaptive Collaborative Filtering
- RECSYS'08
, 2008
"... We present a flexible approach to collaborative filtering which stems from basic research results. The approach is flexible in several dimensions: We introduce an algorithm where the loss can be tailored to a particular recommender problem. This allows us to optimize the prediction quality in a way ..."
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Cited by 10 (1 self)
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desirable properties in terms of privacy needs of users, parallelization of the algorithm as well as collaborative filtering as a service. We evaluate the algorithm on data provided by WikiLens. This data is a cross-domain data set as it contains ratings on items from a vast array of categories. Evaluation
Collaborative Filtering via Group-Structured Dictionary Learning ⋆
"... Abstract. Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experim ..."
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Cited by 2 (0 self)
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Abstract. Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical
Collaborative Filtering via Group-Structured Dictionary Learning
"... • Help users in decision making. 1. ..."
Results 1 - 10
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190
