Latent Dirichlet allocation for linking user-generated content and e-commerce data

Automatic linking of online content improves navigation possibilities for end users. We focus on linking content generated by users to other relevant sites. In particular, we study the problem of linking information between different usages of the same language, e.g., colloquial and formal idioms or the language of consumers versus the language of sellers. The challenge is that the same items are described using very distinct vocabularies. As a case study, we investigate a new task of linking textual Pinterest.com pins (colloquial) to online webshops (formal). Given this task, our key insight is that we can learn associations between formal and informal language by utilizing aligned data and probabilistic modeling. Specifically, we thoroughly evaluate three different modeling paradigms based on probabilistic topic modeling: monolingual latent Dirichlet allocation (LDA), bilingual LDA (BiLDA) and a novel multi-idiomatic LDA model (MiLDA). We compare these to the unigram model with Dirichlet prior. Our results for all three topic models reveal the usefulness of modeling the hidden thematic structure of the data through topics, as opposed to the linking model based solely on the standard unigram. Moreover, our proposed MiLDA model is able to deal with intrinsic multi-idiomatic data by considering the shared vocabulary between the aligned document pairs. The proposed MiLDA obtains the largest stability (less variation with changes in parameters) and highest mean average precision scores in the linking task.