User Acceptance of Recommender Systems: Influence of the Preference Elicitation Algorithm

2014 ◽  
Author(s):  
Marcelo G. Armentano ◽  
Roberto Abalde ◽  
Silvia Schiaffino ◽  
Analia Amandi
Keyword(s):  
Recommender Systems ◽  
User Acceptance ◽  
Preference Elicitation

scholarly journals User-Involved Preference Elicitation for Product Search and Recommender Systems

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 93 ◽  
Author(s):  
Pearl Pu ◽  
Li Chen
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Preference Elicitation ◽  
Design Guidelines ◽  
User Preferences ◽  
The State ◽  
User Preference ◽  
Large Collection ◽  
Complete Model ◽  
Product Search

We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of our paper. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs. As a result, users fail to find the outcomes that best satisfy their needs and preferences. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces. For each area, we describe the state-of-the-art of the developed techniques and discuss concrete scenarios where they have been applied and tested.

scholarly journals A Survey on Conversational Recommender Systems

2021 ◽  
Vol 54 (5) ◽  
pp. 1-36
Author(s):  
Dietmar Jannach ◽  
Ahtsham Manzoor ◽  
Wanling Cai ◽  
Li Chen
Keyword(s):  
Recommender Systems ◽  
Language Processing ◽  
Information Overload ◽  
User Interaction ◽  
Preference Elicitation ◽  
Significant Progress ◽  
Software Applications ◽  
The Past ◽  
Elicitation Process ◽  
Ranked List

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this article, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

scholarly journals Preference elicitation techniques for group recommender systems

2012 ◽  
Vol 189 ◽  
pp. 155-175 ◽  
Author(s):  
Inma Garcia ◽  
Sergio Pajares ◽  
Laura Sebastia ◽  
Eva Onaindia
Keyword(s):  
Recommender Systems ◽  
Preference Elicitation ◽  
Group Recommender Systems ◽  
Group Recommender

On the Importance of Subtext in Recommender Systems

i-com ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Peter Grasch ◽  
Alexander Felfernig
Keyword(s):  
Empirical Study ◽  
Recommender Systems ◽  
Recommender System ◽  
Unsolved Problem ◽  
Preference Elicitation ◽  
User Preferences ◽  
Session Length ◽  
Knowledge Based ◽  
Paralinguistic Information ◽  
Recommendation Strategy

AbstractConversational recommender systems have been shown capable of allowing users to navigate even complex and unknown application domains effectively. However, optimizing preference elicitation remains a largely unsolved problem. In this paper we introduce SPEECHREC, a speech-enabled, knowledge-based recommender system, that engages the user in a natural-language dialog, identifying not only purely factual constraints from the users’ input, but also integrating nuanced lexical qualifiers and paralinguistic information into the recommendation strategy. In order to assess the viability of this concept, we present the results of an empirical study where we compare SPEECHREC to a traditional knowledge-based recommender system and show how incorporating more granular user preferences in the recommendation strategy can increase recommendation quality, while reducing median session length by 46 %.

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