Propagation of content similarity through a collaborative network for live show recommendation

TL;DR

This paper introduces a network-based recommender system for live shows that combines collaborative and content-based filtering, effectively addressing the cold-start problem by propagating content similarity through a user-show network.

Contribution

It proposes a novel method that integrates network alignment and similarity spreading to improve live show recommendations, especially for new shows.

Findings

System performs well on large real-world datasets.

Effective in cold-start scenarios for new shows.

Combines collaborative filtering with content-based similarity propagation.

Abstract

We present a network-based recommender system for live shows (concerts, theater, circus, etc) that finds a set of people probably interested in a given, new show. We combine collaborative and content-based filtering to take benefit of past activity of users and of the features of the new show. Indeed, as this show is new we cannot rely on collaborative filtering only. To solve this cold-start problem, we perform network alignment and insert the new show in a way consistent with collaborative filtering. We refine the obtained similarities using spreading in the network. We illustrate the performances of our system on a large scale real-world dataset.