Multi-objective Topic Modeling for Exploratory Search in Tech News

Exploratory search is a paradigm of information retrieval, in which the user's intention is to learn the subject domain better. To do this the user repeats "query-browse-refine" interactions with the search engine many times. We consider typical exploratory search tasks formulated by long text queries. People usually solve such a task in about half an hour and find dozens of documents using conventional search facilities iteratively. The goal of this paper is to reduce the time-consuming multi-step process to one step without impairing the quality of the search. Probabilistic topic modeling is a suitable text mining technique to retrieve documents, which are semantically relevant to a long text query. We use the additive regularization of topic models (ARTM) to build a model that meets multiple objectives. The model should have sparse, diverse and interpretable topics. Also, it should incorporate meta-data and multimodal data such as n-grams, authors, tags and categories. Balancing the regularization criteria is an important issue for ARTM. We tackle this problem with coordinate-wise optimization technique, which chooses the regularization trajectory automatically. We use the parallel online implementation of ARTM from the open source library BigARTM. Our evaluation technique is based on crowdsourcing and includes two tasks for assessors: the manual exploratory search and the explicit relevance feedback. Experiments on two popular tech news media show that our topic-based exploratory search outperforms assessors as well as simple baselines, achieving precision and recall of about 85-92%.