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.