In this paper, we elicit implicit information needs that arise during the process of deciding which products to purchase on e-commerce (EC) sites.We designed product purchase tasks to capture implicit information needs, and we conducted a user study to collect utterance data using a think-aloud method.By analyzing the utterances of participants during the tasks, we developed a taxonomy comprising five categories where people express preferences for products and 11 categories where people want to understand products.Our taxonomy includes implicit information needs that have not been captured in existing EC-related taxonomies (e.g., Preference for Subjective Attributes and Understanding Product Differences).We revealed the characteristics of each category of information need in terms of timing during the tasks: e.g., the information need of Understanding Product Range occurred very frequently in the early stage of a task.We also revealed the occurrence frequencies for different task types: e.g., the information needs of Preference for Objective Attributes, Understanding Product Range, and Understanding Terminology had a higher occurrence when purchasing products less frequently and at a higher cost than when purchasing products frequently at a relatively low cost.Our taxonomy could be used to further improve users' purchasing processes on EC sites.

Abstract This paper proposes a visualization recommender system for tabular data given visualization intents (e.g., “population trends in Italy” and “smartphone market share”). The proposed method predicts the most suitable visualization type (e.g., line, pie, or bar chart) and visualized columns (columns used for visualization) based on statistical features extracted from the tabular data as well as semantic features derived from the visualization intent. To predict the appropriate visualization type, we propose a bi-directional attention (BiDA) model that identifies important table columns using the visualization intent and important parts of the intent using the table headers. To determine the visualized columns, we employ a pre-trained neural language model to encode both visualization intents and table columns and predict which columns are the most likely to be used for visualization. Since there was no available dataset for this task, we created a new dataset consisting of over 100 K tables and their appropriate visualization. Experiments revealed that our proposed methods accurately predicted suitable visualization types and visualized columns.