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Preference Handling -- An Introductory Tutorial
"... We present a tutorial introduction to the area of preference handling – one of the core issues in the design of any system that automates or supports decision making. The main goal of this tutorial is to provide a framework, or perspective, within which current work on preference handling – represen ..."
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We present a tutorial introduction to the area of preference handling – one of the core issues in the design of any system that automates or supports decision making. The main goal of this tutorial is to provide a framework, or perspective, within which current work on preference handling – representation, reasoning, and elicitation – can be understood. Our intention is not to provide a technical description of the diverse methods used, but rather, to provide a general perspective on the problem and its varied solutions and to highlight central ideas and techniques.
Long-Term and SessionSpecific User Preferences in a Mobile Recommender System
- In Proc. of the 2008 Int’l Conference on Intelligent User Interfaces
, 2008
"... User preferences acquisition plays a very important role for recommender systems. In a previous paper, we proposed a critique-based mobile recommendation methodology exploiting both long-term and session-specific user preferences. In this paper, we evaluate the impact on the recommendation accuracy ..."
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Cited by 8 (3 self)
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User preferences acquisition plays a very important role for recommender systems. In a previous paper, we proposed a critique-based mobile recommendation methodology exploiting both long-term and session-specific user preferences. In this paper, we evaluate the impact on the recommendation accuracy of the two kinds of user preferences. We have ran off-line experiments exploiting the log data recorded in a previous live-user evaluation, and we show here that exploiting both long-term and sessionspecific preferences results in a better recommendation accuracy than using a single user model component. Moreover, we show that when the simulated user behavior deviates from that dictated by the acquired user model the session-specific preferences are more useful than the longterm ones in predicting user decisions.
Replaying Live-User Interactions in the Off-Line Evaluation of Critique-based Mobile Recommendations
- In Proc. of the 2007 ACM Recommender Systems Conference
, 2007
"... Supporting conversational approaches in mobile recommender systems is challenging because of the inherent limitations of mobile devices and the dependence of produced recommendations on the context. In a previous work, we proposed a critique-based mobile recommendation approach and presented the res ..."
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Cited by 6 (6 self)
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Supporting conversational approaches in mobile recommender systems is challenging because of the inherent limitations of mobile devices and the dependence of produced recommendations on the context. In a previous work, we proposed a critique-based mobile recommendation approach and presented the results of a live users evaluation. Live-user evaluations are expensive and there we could not compare different system variants to check all our research hypotheses. In this paper, we present an innovative simulation methodology and its use in the comparison of different user-query representation approaches. Our simulation test procedure replays off-line, against different system variants, interactions recorded in the live-user evaluation. The results of the simulation tests show that the composite query representation, which employs both logical and similarity queries, does improve the recommendation performance over a representation using either a logical or a similarity query.
Relational Preference Rules for Control
- In Proc. the 11th Intern. Conf. on Principles of Knowledge Representation and Reasoning (KR-08
"... Much like relational probabilistic models, the need for relational preference models arises naturally in real-world applications where the set of object classes is fixed, but object instances vary from one application to another as well as within the run-time of a single application. To address this ..."
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Much like relational probabilistic models, the need for relational preference models arises naturally in real-world applications where the set of object classes is fixed, but object instances vary from one application to another as well as within the run-time of a single application. To address this problem, we suggest a rule-based preference specification language. This language extends regular rule-based languages and leads to a much more flexible approach for specifying control rules for autonomous systems. It also extends standard generalized-additive value functions to handle a dynamic universe of objects: given any specific set of objects it induces a generalized-additive value function. Throughout the paper we use the example of a decision support system for command and control centers we are currently developing to motivate the need for such models and to illustrate them.
Helping Users Perceive Recommendation Diversity
"... The recommendation diversity is increasingly being recognized as an important issue in satisfying users ’ needs for recommender systems. Various diversity-enhancing methods have been developed to increase diversity while making personalized recommendations to users. However, one crucial issue remain ..."
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The recommendation diversity is increasingly being recognized as an important issue in satisfying users ’ needs for recommender systems. Various diversity-enhancing methods have been developed to increase diversity while making personalized recommendations to users. However, one crucial issue remains. Could the diversity, as system designers have carefully incorporated, be perceived by users and influence their interaction behaviors? In this paper, we try to investigate whether this issue can be addressed at the interface level. Our goal is to understand design issues that enhance users ’ perception of recommendation diversity and more importantly their satisfaction. A within-subject user study was conducted to compare an organization interface, which groups recommendations into categories, with a standard list interface. Our user study results show that the organization interface indeed effectively increased users ’ perceived diversity of recommendations, especially perceived categorical diversity. Correlation results reveal that the perceived categorical diversity in recommendation lists has a significant correlation with users’ perceived ease of use of a system, perceived usefulness of the system and attitudes towards the system, thereby resulting in a positive effect on their intention to use the system. We conclude by proposing design guidelines based on our study observations. Categories and Subject Descriptors
A Cross-Cultural User Evaluation of Product Recommender Interfaces
"... We present a cross-cultural user evaluation of an organizationbased product recommender interface, by comparing it with the traditional list view. The results show that it performed significantly better, for all study participants, in improving on their competence perceptions, including perceived re ..."
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We present a cross-cultural user evaluation of an organizationbased product recommender interface, by comparing it with the traditional list view. The results show that it performed significantly better, for all study participants, in improving on their competence perceptions, including perceived recommendation quality, perceived ease of use and perceived usefulness, and positively impacting users ’ behavioral intentions such as intention to save effort in the next visit. Additionally, oriental users were observed reacting more significantly strongly to the organization interface regarding some subjective aspects, compared to western subjects. Through this user study, we also identified the dominating role of the recommender system’s decision-aiding competence in stimulating both oriental and western users ’ return intention to an e-commerce website where the system is applied. Categories and Subject Descriptors H.5.2 [Information interfaces and presentation]: User Interfaces – evaluation/methodology, graphical user interfaces (GUI), user-centered design.
Conversational case-based recommendations exploiting a structured case model
- In Proceedings of the 9th European Conference on Case-Based Reasoning
, 2008
"... Abstract. There are case-based recommender systems that generate personalized recommendations for users exploiting the knowledge contained in past recommendation cases. These systems assume that the quality of a new recommendation depends on the quality of the recorded recommendation cases. In this ..."
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Abstract. There are case-based recommender systems that generate personalized recommendations for users exploiting the knowledge contained in past recommendation cases. These systems assume that the quality of a new recommendation depends on the quality of the recorded recommendation cases. In this paper, we present a case model exploited in a mobile critique-based recommender system that generates recommendations using the knowledge contained in previous recommendation cases. The proposed case model is capable of modeling evolving (conversational) recommendation sessions, capturing the recommendation context, supporting critique-based user-system conversations, and integrating both ephemeral and stable user preferences. In this paper, we evaluate the proposed case model through replaying real recommendation cases recorded in a previous live-user evaluation. We measure the impact of the various components of the case model on the system’s recommendation performance. The experimental results show that the case components that model the user’s contextual information, default preferences, and initial preferences, are the most important for mobile context-dependent recommendation. 1
The Evaluation of a Hybrid Critiquing System with Preference-based Recommendations Organization
- 1 st ACM Conference on Recommender Systems
, 2007
"... The critiquing-based recommender system mainly aims to guide users to make an accurate and confident decision, while requiring them to consume a low level of effort. We have previously found that the hybrid critiquing system of combining the strengths from both system-proposed critiques and user sel ..."
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The critiquing-based recommender system mainly aims to guide users to make an accurate and confident decision, while requiring them to consume a low level of effort. We have previously found that the hybrid critiquing system of combining the strengths from both system-proposed critiques and user self-motivated critiquing facility can highly improve users ’ subjective perceptions such as their decision confidence and trusting intentions. In this paper, we continue to investigate how to further reduce users ’ objective decision effort (e.g. time consumption) in such system by increasing the critique prediction accuracy of the system-proposed critiques. By means of real user evaluation, we proved that a new hybrid critiquing system design that integrates the preferencebased recommendations organization technique for critiques suggestion can effectively help to increase the proposed critiques’ application frequency and significantly contribute to saving users’ task time and interaction effort.
Towards Automating Decision Aiding Through Argumentation
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
