Paper Info
Title
On-demand set-based recommendations
Abstract
This paper investigates the problem of generating on-demand recommendations over a dataset of items where the input is a selection of a few of the items. As an example in the context of a movie dataset, the user may wish to see a list of movies related to the three animation movies 'Finding Nemo', 'Up' and 'Spirited Away'. In this case, it would be expected that the list returned would contain other animation movies like 'Wall-E', 'Princess Mononoke' etc. Thus, this problem can be viewed as a type of "clustering on demand" problem [1]. It is the set form of input that distinguishes this problem from a standard information retrieval problem where the query is usually a single item or an abstraction of a single item in the dataset. In this paper, we present several new approaches to dealing with this problem. We also show some representative results on a movie text dataset.
Year
DOI
Venue
2010
10.1145/1864708.1864775
RecSys
Keywords
Field
DocType
princess mononoke,animation movie,representative result,single item,on-demand recommendation,new approach,standard information retrieval problem,on-demand set-based recommendation,finding nemo,movie text dataset,movie dataset,probabilistic model,information retrieval
Data mining,World Wide Web,Abstraction,On demand,Information retrieval,Computer science,Animation,Artificial intelligence,Cluster analysis,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
1
Authors
1
Name
Order
Citations
PageRank
Suhrid Balakrishnan123814.60