3,652 research outputs found
Report on the future conversations workshop at CHIIR 2021
The Future Conversations workshop at CHIIR’21 looked to the future of search, recommen- dation, and information interaction to ask: where are the opportunities for conversational interactions? What do we need to do to get there? Furthermore, who stands to benefit?The workshop was hands-on and interactive. Rather than a series of technical talks, we solicited position statements on opportunities, problems, and solutions in conversational search in all modalities (written, spoken, or multimodal). This paper –co-authored by the organisers and participants of the workshop– summarises the submitted statements and the discussions we had during the two sessions of the workshop. Statements discussed during the workshop are available at https://bit.ly/FutureConversations2021Statements
Enriching Word Embeddings with Food Knowledge for Ingredient Retrieval
Smart assistants and recommender systems must deal with lots of information coming from different sources and having different formats. This is more frequent in text data, which presents increased variability and complexity, and is rather common for conversational assistants or chatbots. Moreover, this issue is very evident in the food and nutrition lexicon, where the semantics present increased variability, namely due to hypernyms and hyponyms. This work describes the creation of a set of word embeddings based on the incorporation of information from a food thesaurus - LanguaL - through retrofitting. The ingredients were classified according to three different facet label groups. Retrofitted embeddings seem to properly encode food-specific knowledge, as shown by an increase on accuracy as compared to generic embeddings (+23%, +10% and +31% per group). Moreover, a weighing mechanism based on TF-IDF was applied to embedding creation before retrofitting, also bringing an increase on accuracy (+5%, +9% and +5% per group). Finally, the approach has been tested with human users in an ingredient retrieval exercise, showing very positive evaluation (77.3% of the volunteer testers preferred this method over a string-based matching algorithm)
A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations
One potential solution to help people change their eating behavior is to develop conversational systems able to recommend healthy recipes. Beyond the intrinsic quality of the recommendations themselves, various factors might also influence users? perception of a recommendation. Two of these factors are the conversational skills of the system and users' interaction modality. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users? eating habits and current preferences. Users can interact with Cora in two different ways. They can select predefined answers by clicking on buttons to talk to Cora or write text in natural language. On the other hand, Cora can engage users through a social dialogue, or go straight to the point. We conduct an experiment to evaluate the impact of Cora's conversational skills and users' interaction mode on users' perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users' perception of the interaction as well as their perception of the system
The interplay between food knowledge, nudges, and preference elicitation methods determines the evaluation of a recipe recommender system
Domain knowledge can affect how a user evaluates different aspects of a recommender system. Recipe recommendations might be difficult to understand, as some health aspects are implicit. The appropriateness of a recommender’s preference elicitation (PE) method, whether users rate individual items or item attributes, may depend on the user’s knowledge level. We present an online recipe recommender experiment. Users (𝑁=360) with varying levels of subjective food knowledge faced different cognitive digital nudges (i.e., food labels) and PE methods. In a 3 (recipes annotated with no labels, Multiple Traffic Light (MTL) labels, or full nutrition labels) x2 (PE method : content-based PE or knowledge-based) between-subjects design. We observed a main effect of knowledge-based PE on the healthiness of chosen recipes, while MTL label only helped marginally. A Structural Equation Model analysis revealed that the interplay between user knowledge and the PE method reduced the perceived effort of using the system and in turn, affected choice difficulty and satisfaction. Moreover, the evaluation of health labels depends on a user’s level of food knowledge. Our findings emphasize the importance of user characteristics in the evaluation of food recommenders and the merit of interface and inter action aspects
Explainable Active Learning for Preference Elicitation
Gaining insights into the preferences of new users and subsequently
personalizing recommendations necessitate managing user interactions
intelligently, namely, posing pertinent questions to elicit valuable
information effectively. In this study, our focus is on a specific scenario of
the cold-start problem, where the recommendation system lacks adequate user
presence or access to other users' data is restricted, obstructing employing
user profiling methods utilizing existing data in the system. We employ Active
Learning (AL) to solve the addressed problem with the objective of maximizing
information acquisition with minimal user effort. AL operates for selecting
informative data from a large unlabeled set to inquire an oracle to label them
and eventually updating a machine learning (ML) model. We operate AL in an
integrated process of unsupervised, semi-supervised, and supervised ML within
an explanatory preference elicitation process. It harvests user feedback (given
for the system's explanations on the presented items) over informative samples
to update an underlying ML model estimating user preferences. The designed user
interaction facilitates personalizing the system by incorporating user feedback
into the ML model and also enhances user trust by refining the system's
explanations on recommendations. We implement the proposed preference
elicitation methodology for food recommendation. We conducted human experiments
to assess its efficacy in the short term and also experimented with several AL
strategies over synthetic user profiles that we created for two food datasets,
aiming for long-term performance analysis. The experimental results demonstrate
the efficiency of the proposed preference elicitation with limited user-labeled
data while also enhancing user trust through accurate explanations.Comment: Preprin
A Survey on Conversational Search and Applications in Biomedicine
This paper aims to provide a radical rundown on Conversation Search
(ConvSearch), an approach to enhance the information retrieval method where
users engage in a dialogue for the information-seeking tasks. In this survey,
we predominantly focused on the human interactive characteristics of the
ConvSearch systems, highlighting the operations of the action modules, likely
the Retrieval system, Question-Answering, and Recommender system. We labeled
various ConvSearch research problems in knowledge bases, natural language
processing, and dialogue management systems along with the action modules. We
further categorized the framework to ConvSearch and the application is directed
toward biomedical and healthcare fields for the utilization of clinical social
technology. Finally, we conclude by talking through the challenges and issues
of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an
integrated and unified vision of the ConvSearch components from different
fields, which benefit the information-seeking process in healthcare systems
“I Will Follow You!” – How Recommendation Modality Impacts Processing Fluency and Purchase Intention
Although conversational agents (CA) are increasingly used for providing purchase recommendations, important design questions remain. Across two experiments we examine with a novel fluency mechanism how recommendation modality (speech vs. text) shapes recommendation evaluation (persuasiveness and risk), the intention to follow the recommendation, and how modality interacts with the style of recommendation explanation (verbal vs. numerical). Findings provide robust evidence that text-based CAs outperform speech-based CAs in terms of processing fluency and consumer responses. They show that numerical explanations increase processing fluency and purchase intention of both recommendation modalities. The results underline the importance of processing fluency for the decision to follow a recommendation and highlight that processing fluency can be actively shaped through design decisions in terms of implementing the right modality and aligning it with the optimal explanation style. For practice, we offer actionable implications on how to make effective sales agents out of CAs
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