2016/08/25 by Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas · 1 citation
Computer Science · #Discriminative model #Feature (linguistics) #Feature vector #Latent variable #Natural Language Processing Techniques #Product (mathematics) #Rank (graph theory) #Relation (database) #Space (punctuation) #Text and Document Classification Technologies #Topic Modeling #Vector space #cs.AI #cs.CL #cs.IR
paper · pdf · doi:10.1145/2983323.2983702
CIKM2016, Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 2016
arxiv created 2016/08/25 · arxiv updated 2016/08/26 · openalex created_date 2016/09/16 · openalex publication_date 2016/10/24 · openalex updated_date 2026/08/05
We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular word. We compare our method to existing latent vector space models (LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank setting. Our latent vector space model achieves its enhanced performance as it learns better product representations. Furthermore, the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation. We provide an in-depth analysis of the performance of our model and analyze the structure of the learned representations.