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In many applications, flexibility of recommendation, which is the capability of handling multiple dimensions and various recommendation types, is very important. In this paper, we focus on the flexibility of recommendation and propose a graph-based multidimensional recommendation method. We consider the problem as an entity ranking problem on the graph(More)
In this paper, we propose an object ranking method for search and recommendation. By selecting schema-level paths and following them in an entity-relationship graph, it can incorporate diverse semantics existing in the graph. Utilizing this kind of graph-based data models has been recognized as a reasonable way for dealing with heterogeneous data. However,(More)
So far, many researchers have worked on recommender systems using users' implicit feedback, since it is difficult to collect explicit item preferences in most applications. Existing researches generally use a pseudo-rating matrix by adding up the number of item consumption; however, this naive approach may not capture user preferences correctly in that many(More)
As network and computing technologies have improved, and the number of mobile devices has increased, realizing context-aware personal services has become one of the most important issues in pervasive computing. Semantic technology may help acquiring, organizing, and processing context information. However, most approaches thus far have adopted semantic(More)
—In this paper, we aim to overcome barriers to realizing practical lifelog systems, and we propose a lifelog system named 'LifeLogOn' (Lifelog Ontology; 'Log on to your lifelogs'). The system allows users to easily build and exploit lifelog ontology by importing various available data sources. In addition, we present some use case scenarios to show how our(More)