The paper is concerned with relation prediction in multi-relational domains using matrix factorization. While most past predictive models focussed on one single relation type between two entity types, in the paper a generalized model is presented that is able to deal with an arbitrary number of relation types and entity types in a domain of interest. The novel multi-relational matrix factorization is domain independent and highly scalable. We validate the performance of our approach using two real-world data sets, i.e. user-movie recommendations and gene function prediction
Abstract. This paper aims at the problem of link pattern prediction in collections of objects connec...
University of Minnesota Ph.D. dissertation. June 2012. Major: Computer science. Advisor:Arindam Bane...
With the rising of Internet as well as modern social media, relational data has become ubiquitous, w...
Multi-matrix factorization models provide a scalable and ef-fective approach for multi-relational le...
Many real-world domains are relational in nature, consisting of a set of objects related to each oth...
Abstract. This paper introduces a new stepwise approach for predict-ing one specific binary relation...
The revolution of social networks and methods of analyzing them have attracted interest in many rese...
Learning good representations on multi-relational graphs is essential to knowledge base completion (...
Traditional relation extraction predicts relations within some fixed and finite target schema. Machi...
The primary difference between propositional (attribute-value) and relational data is the existence ...
The revolution of social networks and methods of analyzing them have attracted interest in many rese...
The world around us is composed of entities, each having various properties and participating in rel...
Matrix factorization has found incredible success and widespread application as a collaborative filt...
Abstract. Determining the functions of genes is essential for under-standing how the metabolisms wor...
This thesis is a comprehensive study of matrix factorization methods used in recommender systems. We...
Abstract. This paper aims at the problem of link pattern prediction in collections of objects connec...
University of Minnesota Ph.D. dissertation. June 2012. Major: Computer science. Advisor:Arindam Bane...
With the rising of Internet as well as modern social media, relational data has become ubiquitous, w...
Multi-matrix factorization models provide a scalable and ef-fective approach for multi-relational le...
Many real-world domains are relational in nature, consisting of a set of objects related to each oth...
Abstract. This paper introduces a new stepwise approach for predict-ing one specific binary relation...
The revolution of social networks and methods of analyzing them have attracted interest in many rese...
Learning good representations on multi-relational graphs is essential to knowledge base completion (...
Traditional relation extraction predicts relations within some fixed and finite target schema. Machi...
The primary difference between propositional (attribute-value) and relational data is the existence ...
The revolution of social networks and methods of analyzing them have attracted interest in many rese...
The world around us is composed of entities, each having various properties and participating in rel...
Matrix factorization has found incredible success and widespread application as a collaborative filt...
Abstract. Determining the functions of genes is essential for under-standing how the metabolisms wor...
This thesis is a comprehensive study of matrix factorization methods used in recommender systems. We...
Abstract. This paper aims at the problem of link pattern prediction in collections of objects connec...
University of Minnesota Ph.D. dissertation. June 2012. Major: Computer science. Advisor:Arindam Bane...
With the rising of Internet as well as modern social media, relational data has become ubiquitous, w...