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76
Using linguistic cues for the automatic recognition of personality in conversation and text
- Journal of Artificial Intelligence Research (JAIR
, 2007
"... It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker’s personality traits, the most fundamental dimension of variation between humans. Recent work explores the automat ..."
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Cited by 98 (4 self)
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It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker’s personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models
Preference-based search using example-critiquing with suggestions
- Journal of Artificial Intelligence Research
, 2006
"... We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We ..."
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Cited by 47 (4 self)
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We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users’ current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%. 1.
A survey of explanations in recommender systems
- In Data Engineering Workshop, 2007 IEEE 23rd International Conference on
, 2007
"... This paper provides a comprehensive review of explanations in recommender systems. We highlight seven possible advantages of an explanation facility, and describe how existing measures can be used to evaluate the quality of explanations. Since explanations are not independent of the recommendation p ..."
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Cited by 43 (4 self)
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This paper provides a comprehensive review of explanations in recommender systems. We highlight seven possible advantages of an explanation facility, and describe how existing measures can be used to evaluate the quality of explanations. Since explanations are not independent of the recommendation process, we consider how the ways recommendations are presented may affect explanations. Next, we look at different ways of interacting with explanations. The paper is illustrated with examples of explanations throughout, where possible from existing applications. 1
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
, 2007
"... The complexity of product assortments offered by online selling platforms makes the selection of appropriate items a challenging task. Customers can differ significantly in their expertise and level of knowledge regarding such product assortments. Consequently, intelligent recommender systems are re ..."
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Cited by 35 (18 self)
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The complexity of product assortments offered by online selling platforms makes the selection of appropriate items a challenging task. Customers can differ significantly in their expertise and level of knowledge regarding such product assortments. Consequently, intelligent recommender systems are required which provide personalized dialogues supporting the customer in the product selection process. In this paper we present the domainindependent, knowledge-based recommender environment CWAdvisor which assists users by guaranteeing the consistency and appropriateness of solutions, by identifying additional selling opportunities, and by providing explanations for solutions. Using examples from different application domains, we show how model-based diagnosis, personalization, and intuitive knowledge acquisition techniques support the effective implementation of customer-oriented sales dialogues. In this context, we report our experiences gained in industrial projects and present an evaluation of successfully deployed recommender applications.
Adaptive Cognitive Orthotics: Combining Reinforcement Learning and Constraint-based Temporal Reasoning
- In Proceedings of the 21st International Conference on Machine Learning
, 2004
"... Reminder systems support people with impaired prospective memory and/or executive function, by providing them with reminders of their functional daily activities. We integrate temporal constraint reasoning with reinforcement learning (RL) to build an adaptive reminder system and in a simulated envir ..."
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Cited by 22 (4 self)
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Reminder systems support people with impaired prospective memory and/or executive function, by providing them with reminders of their functional daily activities. We integrate temporal constraint reasoning with reinforcement learning (RL) to build an adaptive reminder system and in a simulated environment demonstrate that it can personalize to a user and adapt to both short- and long-term changes. In addition to advancing the application domain, our integrated algorithm contributes to research on temporal constraint reasoning by showing how RL can select an optimal policy from amongst a set of temporally consistent ones, and it contributes to the work on RL by showing how temporal constraint reasoning can be used to dramatically reduce the space of actions from which an RL agent needs to learn. 1.
Preference-based Organization Interfaces: Aiding User Critiques in Recommender Systems
"... Abstract. Users ’ critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system’s accuracy in recommendations that may better interest the user. In this paper, we present a novel approach to assist users in making critiques acco ..."
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Cited by 18 (8 self)
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Abstract. Users ’ critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system’s accuracy in recommendations that may better interest the user. In this paper, we present a novel approach to assist users in making critiques according to their stated and potentially hidden preferences. This approach is derived from our previous work on critique generation and organization techniques. Based on a collection of real user data, we conducted an experiment to compare our approach with three existing critique generation systems. Results show that our preference-based organization interface achieves the highest level of prediction accuracy in suggesting users ’ intended critiques and recommendation accuracy in locating users ’ target choices. In addition, it can potentially most efficiently save real users ’ interaction effort in decision making.
Learning and Adaptivity in Interactive Recommender Systems ABSTRACT
"... Recommender systems are intelligent E-commerce applications that assist users in a decision-making process by offering personalized product recommendations during an interaction session. Quite recently, conversational approaches have been introduced in order to support more interactive recommendatio ..."
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Cited by 17 (5 self)
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Recommender systems are intelligent E-commerce applications that assist users in a decision-making process by offering personalized product recommendations during an interaction session. Quite recently, conversational approaches have been introduced in order to support more interactive recommendation sessions. Notwithstanding the increased interactivity offered by these approaches, the system employs an interaction strategy that is specified apriori (at design time) and followed quite rigidly during the interaction. In this paper, we present a new type of recommender system which is capable of learning autonomously an adaptive interaction strategy for assisting the users in acquiring their interaction goals. We view the recommendation process as a sequential decision problem and we model it as a Markov Decision Process (MDP). We learn a model of the user behavior, and use it to acquire the adaptive strategy using Reinforcement Learning (RL) techniques. In this context, the system learns the optimal strategy by observing the consequences of its actions on the users and also on the final outcome of the recommendation session. We apply our approach within an existing travel recommender system which uses a rigid, non-adaptive support strategy for advising a user in refining a query to a travel product catalogue. The initial results demonstrate the value of our approach and show that our system is able to improve the non-adaptive strategy in order to learn an optimal (adaptive) recommendation strategy.
ABSTRACT Supporting Product Selection with Query Editing Recommendations
"... Consider a conversational product recommender system in which a user repeatedly edits and resubmits a query until she finds a product that she wants. We show how an advisor can: observe the user’s actions; infer constraints on the user’s utility function and add them to a user model; use the constra ..."
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Cited by 17 (8 self)
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Consider a conversational product recommender system in which a user repeatedly edits and resubmits a query until she finds a product that she wants. We show how an advisor can: observe the user’s actions; infer constraints on the user’s utility function and add them to a user model; use the constraints to deduce which queries the user is likely to try next; and advise the user to avoid those that are unsatisfiable. We call this information recommendation. We give a detailed formulation of information recommendation for the case of products that are described by a set of Boolean features. Our experimental results show that if the user is given advice, the number of queries she needs to try before finding the product of highest utility is greatly reduced. We also show that an advisor that confines its advice to queries that the user model predicts are likely to be tried next will give shorter advice than one whose advice is unconstrained by the user model.
Improving Recommender Systems with Adaptive Conversational Strategies
"... Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformu ..."
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Cited by 16 (1 self)
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Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users ’ behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions. Categories and Subject Descriptors
Personalized User Preference Elicitation for e-Services
- Proc. of the 2005 IEEE International Conference on e-Technology, e-Commerce, and e-Service, Hong Kong
, 2005
"... The quality of the results produced by personalized e-service applications like product recommenders, buying advisory applications, or product configurators is strongly determined by the accuracy of the system’s estimate of the individual customer’s real needs and preferences. In particular in domai ..."
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Cited by 15 (8 self)
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The quality of the results produced by personalized e-service applications like product recommenders, buying advisory applications, or product configurators is strongly determined by the accuracy of the system’s estimate of the individual customer’s real needs and preferences. In particular in domains where customers cannot be classified automatically, e.g., based on past buying behavior, these needs have to be interactively elicited by questioning the user. In many existing systems only a “one-style-fits-all ” approach based on static fill-out forms is chosen. However, this does not take the user’s background or capabilities into account, which consequently leads to a poor quality of the acquired user model. In this paper, we show how extensive personalization of the user preference elicitation process itself can significantly improve the accuracy of interactively acquired user models. A comprehensive view on adaptation and personalization opportunities in the elicitation process is developed and corresponding examples for the domain of interactive buying advisory are given. The presented personalization and adaptation techniques are implemented in a domain-independent software framework for building interactive advisory applications. We describe specific architectural requirements for such a system and discuss results from various real-world applications. 1.
