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46
Cross-Domain Recommendation via Cluster-Level Latent Factor Model
"... Abstract. Recommender systems always aim to provide recommendations for a user based on historical ratings collected from a single domain (e.g., movies or books) only, which may suffer from the data sparsity problem. Recently, several recommendation models have been proposed to transfer knowledge by ..."
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Cited by 7 (1 self)
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, and diversity among the related domains might outweigh the advantages of such common pattern, which may result in performance degradations. In this paper, we propose a novel cluster-level based latent factor model to enhance the cross-domain recommendation, which can not only learn the common rating pattern
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
Pairwise Cross-Domain Factor Model for Heterogeneous Transfer Ranking ABSTRACT
"... Learning to rank arises in many information retrieval applications, ranging from Web search engine, online advertising to recommendation systems. Traditional ranking mainly focuses on one type of data source, and effective modeling relies on a sufficiently large number of labeled examples, which req ..."
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Cited by 1 (0 self)
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learns latent factors(features) for multi-domain data in partially-overlapped heterogeneous feature spaces. It is capable of learning homogeneous feature correlation, heterogeneous feature correlation, and pairwise preference correlation for cross-domain knowledge transfer. We also derive two PCDF
xx Exploiting Social Tags for Cross-Domain Collaborative Filtering
"... One of the most challenging problems in recommender systems based on the collaborative filtering (CF) concept is data sparseness, i.e., limited user preference data is available for making recommendations. Cross-domain collaborative filtering (CDCF) has been studied as an effective mechanism to alle ..."
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One of the most challenging problems in recommender systems based on the collaborative filtering (CF) concept is data sparseness, i.e., limited user preference data is available for making recommendations. Cross-domain collaborative filtering (CDCF) has been studied as an effective mechanism
Improving Cross-domain Recommendation through Probabilistic Cluster-level Latent Factor Model — Extended Version
"... Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the spar-sity problem appearing in single rating domains. How-ever, previous models only assume that multiple do-mains share a latent common rating p ..."
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Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the spar-sity problem appearing in single rating domains. How-ever, previous models only assume that multiple do-mains share a latent common rating
Selective Transfer Learning for Cross Domain Recommendation
"... Collaborative filtering (CF) aims to predict users ’ ratings on items according to historical user-item preference data. In many realworld applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show th ..."
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Cited by 5 (0 self)
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that by transferring knowledge from some manually selected source domains, the data sparseness problem could be mitigated. However for most cases, parts of source domain data are not consistent with the observations in the target domain, which may misguide the target domain model building. In this paper, we propose a
Cross Domain Recommendation Using Vector Space Transfer Learning
"... ABSTRACT The cold start problem, frequent with recommender systems, addresses the issue in cases where we don't know enough about our users (e.g., the user hasn't rated anything yet, or there are no user activities) in that specific domain. In our paper we present a simple and robust tran ..."
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ABSTRACT The cold start problem, frequent with recommender systems, addresses the issue in cases where we don't know enough about our users (e.g., the user hasn't rated anything yet, or there are no user activities) in that specific domain. In our paper we present a simple and robust
" Enhancing Cross Domain Recommendation with Domain Dependent Tags
"... Abstract-One challenge in recommender system is to deal with data sparsity. To handle this issue, social tags are utilized to bring disjoint domains together for knowledge transfer in cross-domain recommendation. The most intuitive way is to use common tags that present in both source and target do ..."
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Abstract-One challenge in recommender system is to deal with data sparsity. To handle this issue, social tags are utilized to bring disjoint domains together for knowledge transfer in cross-domain recommendation. The most intuitive way is to use common tags that present in both source and target
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
TALMUD – Transfer Learning for Multiple Domains
"... Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Lea ..."
Abstract
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Cited by 6 (0 self)
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Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine
Results 1 - 10
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46
