2020/05/26 by Jacopo Tagliabue, Tagliabue, Jacopo, Bingqing Yu +3
Computer Science · #Advanced Text Analysis Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Recommender Systems and Techniques #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2005.12781
openalex publication_date 2020/05/26 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
In an attempt to balance precision and recall in the search page, leading\ndigital shops have been effectively nudging users into select category facets\nas early as in the type-ahead suggestions. In this work, we present\nSessionPath, a novel neural network model that improves facet suggestions on\ntwo counts: first, the model is able to leverage session embeddings to provide\nscalable personalization; second, SessionPath predicts facets by explicitly\nproducing a probability distribution at each node in the taxonomy path. We\nbenchmark SessionPath on two partnering shops against count-based and neural\nmodels, and show how business requirements and model behavior can be combined\nin a principled way.\n