The Wayback Machine - https://webcf.waybackmachine.org/web/20211023145257/https://dl.acm.org/toc/tois/2021/39/4
Volume 39, Issue 4October 2021Issue-in-Progress
Bibliometrics

SECTION: Special Section on Conversational Search and Recommendation

introduction
Conversational Search and Recommendation: Introduction to the Special Issue
October 2021, Article No.: 38, pp 1–6https://doi.org/10.1145/3465272
research-article
Theories of Conversation for Conversational IR
October 2021, Article No.: 39, pp 1–23https://doi.org/10.1145/3439869

Conversational information retrieval is a relatively new and fast-developing research area, but conversation itself has been well studied for decades. Researchers have analysed linguistic phenomena such as structure and semantics but also paralinguistic ...

research-article
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-start Users
October 2021, Article No.: 40, pp 1–29https://doi.org/10.1145/3446427

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by ...

research-article
Integrating Collaboration and Leadership in Conversational Group Recommender Systems
October 2021, Article No.: 41, pp 1–32https://doi.org/10.1145/3462759

Recent observational studies highlight the importance of considering the interactions between users in the group recommendation process, but to date their integration has been marginal. In this article, we propose a collaborative model based on the social ...

research-article
Why or Why Not? The Effect of Justification Styles on Chatbot Recommendations
October 2021, Article No.: 42, pp 1–21https://doi.org/10.1145/3441715

Chatbots or conversational recommenders have gained increasing popularity as a new paradigm for Recommender Systems (RS). Prior work on RS showed that providing explanations can improve transparency and trust, which are critical for the adoption of RS. ...

research-article
Target-guided Emotion-aware Chat Machine
October 2021, Article No.: 43, pp 1–24https://doi.org/10.1145/3456414

The consistency of a response to a given post at the semantic level and emotional level is essential for a dialogue system to deliver humanlike interactions. However, this challenge is not well addressed in the literature, since most of the approaches ...

research-article
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues
October 2021, Article No.: 44, pp 1–28https://doi.org/10.1145/3462207

Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is challenging in three aspects: (1) the meaning of a context–response pair is built upon language units from multiple granularities (...

research-article
Dialogue History Matters! Personalized Response Selection in Multi-Turn Retrieval-Based Chatbots
October 2021, Article No.: 45, pp 1–25https://doi.org/10.1145/3453183

Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context utterances and response. However, in real-place conversation scenarios, whether a ...

research-article
MyrrorBot: A Digital Assistant Based on Holistic User Models for Personalized Access to Online Services
October 2021, Article No.: 46, pp 1–34https://doi.org/10.1145/3447679

In this article, we present MyrrorBot, a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, andfood recommendations, in a personalized way, by exploiting a ...

research-article
Conversations with Search Engines: SERP-based Conversational Response Generation
October 2021, Article No.: 47, pp 1–29https://doi.org/10.1145/3432726

In this article, we address the problem of answering complex information needs by conducting conversations with search engines, in the sense that users can express their queries in natural language and directly receive the information they need from a ...

    research-article
    Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting
    October 2021, Article No.: 48, pp 1–29https://doi.org/10.1145/3446426

    Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad hoc information retrieval (IR) systems due to the coreference and omission resolution problems ...

    research-article
    A Large-scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search
    October 2021, Article No.: 49, pp 1–32https://doi.org/10.1145/3466796

    Conversational search is a relatively young area of research that aims at automating an information-seeking dialogue. In this article, we help to position it with respect to other research areas within conversational artificial intelligence (AI) by ...

    research-article
    Meta-Information in Conversational Search
    October 2021, Article No.: 50, pp 1–44https://doi.org/10.1145/3468868

    The exchange of meta-information has always formed part of information behavior. In this article, we show that this rule also extends to conversational search. Information about the user’s information need, their preferences, and the quality of search ...

    research-article
    How Am I Doing?: Evaluating Conversational Search Systems Offline
    October 2021, Article No.: 51, pp 1–22https://doi.org/10.1145/3451160

    As conversational agents like Siri and Alexa gain in popularity and use, conversation is becoming a more and more important mode of interaction for search. Conversational search shares some features with traditional search, but differs in some important ...

    research-article
    Meta-evaluation of Conversational Search Evaluation Metrics
    October 2021, Article No.: 52, pp 1–42https://doi.org/10.1145/3445029

    Conversational search systems, such as Google assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging, given that any natural language ...

    research-article
    From Users’ Intentions to IF-THEN Rules in the Internet of Things
    October 2021, Article No.: 53, pp 1–33https://doi.org/10.1145/3447264

    In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN ...

    research-article
    Multi-Response Awareness for Retrieval-Based Conversations: Respond with Diversity via Dynamic Representation Learning
    October 2021, Article No.: 54, pp 1–29https://doi.org/10.1145/3470450

    Conversational systems now attract great attention due to their promising potential and commercial values. To build a conversational system with moderate intelligence is challenging and requires big (conversational) data, as well as interdisciplinary ...

    Subjects

    Users and interactive retrievalDiscourse, dialogue and pragmaticsRecommender systemsInformation retrievalNatural language processingPersonalizationRetrieval models and rankingArtificial intelligenceCollaborative filteringCollaborative filteringContent analysis and feature selectionEmpirical studies in interaction designEnvironment-specific retrievalEvaluation of retrieval resultsInteraction paradigmsMachine learning algorithmsNatural language generationNatural language interfacesQuestion answeringRetrieval effectivenessRetrieval tasks and goalsSocial recommendationTask modelsTest collectionsUser modelsUser studies

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