Results 1 - 10 of 2,456

Cross-domain collaboration recommendation

by Jie Tang, Sen Wu, Jimeng Sun, Hang Su - In KDD’12 , 2012
"... Interdisciplinary collaborations have generated huge impact to society. However, it is often hard for researchers to establish such cross-domain collaborations. What are the patterns of cross-domain collaborations? How do those collaborations form? Can we predict this type of collaborations? Cross-d ..."
Abstract - Cited by 23 (8 self) - Add to MetaCart
topics are focused on a subset of topics. All these patterns violate fundamental assumptions of traditional recommendation systems. In this paper, we analyze the cross-domain collaboration data from research publications and confirm the above patterns. We propose the Cross-domain Topic Learning (CTL

Cross-domain Collaboration Recommendation

by unknown authors
"... Interdisciplinary collaborations have generated huge impact to so-ciety. However, it is often hard for researchers to establish such cross-domain collaborations. What are the patterns of cross-domain collaborations? How do those collaborations form? Can we predict this type of collaborations? Cross- ..."
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collaboration topics are focused on a subset of topics. All these patterns violate fundamental assumptions of traditional recommendation systems. In this paper, we analyze the cross-domain collaboration data from research publications and confirm the above patterns. We propose the Cross-domain Topic Learning

An Efficient Boosting Algorithm for Combining Preferences

by Raj Dharmarajan Iyer , Jr. , 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
Abstract - Cited by 727 (18 self) - Add to MetaCart
search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing Rank

Factorization meets the neighborhood: a multifaceted collaborative filtering model

by Yehuda Koren - In Proc. of the 14th ACM SIGKDD conference , 2008
"... Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent f ..."
Abstract - Cited by 424 (12 self) - Add to MetaCart
Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent

Eigentaste: A Constant Time Collaborative Filtering Algorithm

by Ken Goldberg, Theresa Roeder, Dhruv Gupta, Chris Perkins , 2000
"... Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clusterin ..."
Abstract - Cited by 378 (6 self) - Add to MetaCart
Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline

Personalized Recommendation via Cross-Domain Triadic Factorization

by Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, Can Zhu
"... 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 ..."
Abstract - Cited by 8 (0 self) - Add to MetaCart
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

Cross-Domain Collaborative Filtering over Time

by Bin Li, Xingquan Zhu, Chengqi Zhang, Xiangyang Xue, Xindong Wu - PROCEEDINGS OF THE TWENTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... Collaborative filtering (CF) techniques recommend items to users based on their historical ratings. In real-world scenarios, user interests may drift over time since they are affected by moods, contexts, and pop culture trends. This leads to the fact that a user’s historical ratings comprise many as ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
groups drift slightly between successive temporal domains. The derived method is based on a Bayesian latent factor model which can be inferred using Gibbs sampling. Our experimental results show that our method can achieve state-of-the-art recommendation performance as well as explicitly track

Cross-domain mediation in collaborative filtering

by Shlomo Berkovsky, Tsvi Kuflik, Francesco Ricci - User Modeling, volume 4511 of Lecture Notes in Computer Science , 2007
"... Abstract. One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing and a ..."
Abstract - Cited by 26 (1 self) - Add to MetaCart
Abstract. One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing

Pairwise Cross-Domain Factor Model for Heterogeneous Transfer Ranking ABSTRACT

by Bo Long, Anlei Dong, Yi Chang
"... 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 ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
little. Theretofore, how to leverage labeled information from related heterogeneous domain to improve ranking in a target domain has become a problem of great interests. In this paper, we propose a novel probabilistic model, pairwise cross-domain factor model, to address this problem. The proposed model

Pranking with Ranking

by Koby Crammer, Yoram Singer - Advances in Neural Information Processing Systems 14 , 2001
"... We discuss the problem of ranking instances. In our framework each instance is associated with a rank or a rating, which is an integer from 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank which is as close as possible to the instance's true rank. We describe ..."
Abstract - Cited by 222 (5 self) - Add to MetaCart
describe a simple and efficient online algorithm, analyze its performance in the mistake bound model, and prove its correctness. We describe two sets of experiments, with synthetic data and with the EachMovie dataset for collaborative filtering. In the experiments we performed, our algorithm outperforms
Results 1 - 10 of 2,456