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Eugene Agichtein

Researcher at Emory University

Publications -  166
Citations -  11564

Eugene Agichtein is an academic researcher from Emory University. The author has contributed to research in topics: Question answering & Web search query. The author has an hindex of 47, co-authored 166 publications receiving 10917 citations. Previous affiliations of Eugene Agichtein include Amazon.com & Microsoft.

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Proceedings ArticleDOI

APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization

TL;DR: Zhang et al. as mentioned in this paper proposed a novel deep neural model named attentive pseudo relevance feedback network (APRF-Net) to enhance the representation of rare queries for query categorization, which can be used to select the relevant fine-grained product categories in a product taxonomy.
Proceedings ArticleDOI

Quantifying the Effects of Prosody Modulation on User Engagement and Satisfaction in Conversational Systems

TL;DR: A large-scale empirical study measures the effects of prosodic modulation on user behavior and engagement across multiple conversation domains, both immediately aftereach turn, and at the overall conversation level, and indicates that the prosody modulation significantly increases both immediate and overall user satisfaction.
Proceedings ArticleDOI

VoiSeR: A new benchmark for voice-based search refinement

Abstract: Voice assistants, e.g., Alexa or Google Assistant, have dramatically improved in recent years. Supporting voice-based search, exploration, and refinement are fundamental tasks for voice assistants, and remain an open challenge. For example, when using voice to search an online shopping site, a user often needs to refine their search by some aspect or facet. This common user intent is usually available through a “filter-by” interface on online shopping websites, but is challenging to support naturally via voice, as the intent of refinements must be interpreted in the context of the original search, the initial results, and the available product catalogue facets. To our knowledge, no benchmark dataset exists for training or validating such contextual search understanding models. To bridge this gap, we introduce the first large-scale dataset of voice-based search refinements, VoiSeR, consisting of about 10,000 search refinement utterances, collected using a novel crowdsourcing task. These utterances are intended to refine a previous search, with respect to a search facet or attribute (e.g., brand, color, review rating, etc.), and are manually annotated with the specific intent. This paper reports qualitative and empirical insights into the most common and challenging types of refinements that a voice-based conversational search system must support. As we show, VoiSeR can support research in conversational query understanding, contextual user intent prediction, and other conversational search topics to facilitate the development of conversational search systems.
Proceedings ArticleDOI

RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search

TL;DR: In this paper, a reinforcement learning-based approach, RLIrank, is proposed to support dynamic search. But it requires the user feedback received, and the information displayed so far.