Name
Abstract | ||
|---|---|---|
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 which is constructed using an implicit feedback dataset (e.g. music listening log), and we adapt Personalized PageRank algorithm to rank entities according to a given query that is represented as a set of entities in the graph. Our model has advantages in that not only can it support the flexibility, but also it can take advantage of exploiting indirect relationships in the graph so that it can perform competitively with the other existing recommendation methods without suffering from the sparsity problem. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2043932.2043952 | RecSys |
Keywords | Field | DocType |
indirect relationship,sparsity problem,graph-based multidimensional recommendation method,random walk,various recommendation type,existing recommendation method,multiple dimension,implicit feedback dataset,entity ranking problem,pagerank algorithm,recommender systems,recommender system,random walks,multidimensional | Recommender system,Data mining,Graph,Ranking,Computer science,Random walk,Active listening,Pagerank algorithm,Context awareness,Artificial intelligence,Machine learning,Multiple time dimensions | Conference |
Citations | PageRank | References |
42 | 1.34 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sangkeun Lee | 1 | 498 | 65.59 |
| Sang-il Song | 2 | 45 | 1.76 |
| Minsuk Kahng | 3 | 285 | 19.89 |
| Dongjoo Lee | 4 | 182 | 12.87 |
| Sang-goo Lee | 5 | 832 | 151.04 |