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User-Controllable Learning of Security and Privacy Policies
"... Studies have shown that users have great difficulty specifying their security and privacy policies in a variety of application domains. While machine learning techniques have successfully been used to refine models of user preferences, such as in recommender systems, they are generally configured as ..."
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Studies have shown that users have great difficulty specifying their security and privacy policies in a variety of application domains. While machine learning techniques have successfully been used to refine models of user preferences, such as in recommender systems, they are generally configured as “black boxes ” that take control over the entire policy and severely restrict the ways in which the user can manipulate it. This article presents an alternative approach, referred to as user-controllable policy learning. It involves the incremental manipulation of policies in a context where system and user refine a common policy model. The user regularly provides feedback on decisions made based on the current policy. This feedback is used to identify (learn) incremental policy improvements which are presented as suggestions to the user. The user, in turn, can review these suggestions and decide which, if any, to accept. The incremental nature of the suggestions enhances usability, and because the user and the system manipulate a common policy representation, the user retains control and can still make policy modifications by hand. Results obtained using a neighborhood search implementation of this approach are presented in the context of data derived from the deployment of a friend finder application, where users can share their locations with others, subject to privacy policies they refine over time. We present results showing policy accuracy, which averages 60 % upon initial definition by our users, climbing as high as 90 % using our technique.
Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets
"... Bayesian approaches to utility elicitation typically adopt (myopic) expected value of information (EVOI) as a natural criterion for selecting queries. However, EVOI-optimization is usually computationally prohibitive. In this paper, we examine EVOI optimization using choice queries, queries in which ..."
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Cited by 20 (2 self)
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Bayesian approaches to utility elicitation typically adopt (myopic) expected value of information (EVOI) as a natural criterion for selecting queries. However, EVOI-optimization is usually computationally prohibitive. In this paper, we examine EVOI optimization using choice queries, queries in which a user is ask to select her most preferred product from a set. We show that, under very general assumptions, the optimal choice query w.r.t. EVOI coincides with the optimal recommendation set, that is, a set maximizing the expected utility of the user selection. Since recommendation set optimization is a simpler, submodular problem, this can greatly reduce the complexity of both exact and approximate (greedy) computation of optimal choice queries. We also examine the case where user responses to choice queries are error-prone (using both constant and mixed multinomial logit noise models) and provide worst-case guarantees. Finally we present a local search technique for query optimization that works extremely well with large outcome spaces. 1
Case-studies on exploiting explicit customer requirements in recommender systems
- USER MODELING AND USER-ADAPTED INTERACTION: THE JOURNAL OF PERSONALIZATION RESEARCH, A. TUZHILIN AND B. MOBASHER (EDS.): SPECIAL ISSUE ON DATA MINING FOR PERSONALIZATION
, 2009
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Assessing Regret-based Preference Elicitation with the UTPREF Recommendation System
"... Product recommendation and decision support systems must generally develop a model of user preferences by querying or otherwise interacting with a user. Recent approaches to elicitation using minimax regret have proven to be very powerful in simulation. In this work, we test both the effectiveness o ..."
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Cited by 12 (6 self)
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Product recommendation and decision support systems must generally develop a model of user preferences by querying or otherwise interacting with a user. Recent approaches to elicitation using minimax regret have proven to be very powerful in simulation. In this work, we test both the effectiveness of regret-based elicitation, and user comprehension and acceptance of minimax regret in user studies. We report on a study involving 40 users interacting with the UTPREF Recommendation System, which helps students navigate and find rental accommodation. UTPREF maintains an explicit (but incomplete) generalized additive utility (GAI) model of user preferences, and uses minimax regret for recommendation. We assess the following general questions: How effective is regret-based elicitation in finding optimal or near-optimal products? Do users understand and accept the minimax regret criterion in practice? Do decision-theoretically valid queries for GAI models result in more accurate assessment than simpler, ad hoc queries? On the first two issues, we find that the minimax regret decision criterion is effective, understandable, and intuitively appealing. On the third issue, we find that simple, semantically ambiguous query types perform as well as more demanding, semantically valid queries for GAI models. We also assess the relative difficulty of specific query types.
User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models
"... With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy concerns arise as people start to widely adopt these applications. Users will need to maintain policies to determine under wh ..."
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Cited by 11 (3 self)
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With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy concerns arise as people start to widely adopt these applications. Users will need to maintain policies to determine under which circumstances to share their location. Specifying these policies however, is a cumbersome task, suggesting that machine learning might be helpful. In this paper, we present a user-controllable method for learning location sharing policies. We use a classifier based on multivariate Gaussian mixtures that is suitably modified so as to restrict the evolution of the underlying policy to favor incremental and therefore human-understandable changes as new data arrives. We evaluate the model on real location-sharing policies collected from a live location-sharing social network, and we show that our method can learn policies in a user-controllable setting that are just as accurate as policies that do not evolve incrementally. Additionally, we highlight the strength of the generative modeling approach we take, by showing how our model easily extends to the semi-supervised setting.
Hybrid Critiquing-based Recommender Systems
- IN PROC. IUI (2007)
, 2007
"... We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that the integration of such selfmotivated criti ..."
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Cited by 11 (7 self)
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We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that the integration of such selfmotivated critiquing support enables users to achieve a higher level of decision accuracy while consuming less cognitive effort. In addition, users expressed higher subjective opinions of the hybrid critiquing interface than the interface simply providing system-proposed critiques, and they would more likely return to it for future use.
Elicitation of Factored Utilities
, 2008
"... The effective tailoring of decisions to the needs and desires of specific users requires automated mechanisms for preference assessment. We provide a brief overview of recent direct preference elicitation methods: these methods ask users to answer (ideally, a small number of) queries regarding the ..."
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Cited by 10 (2 self)
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The effective tailoring of decisions to the needs and desires of specific users requires automated mechanisms for preference assessment. We provide a brief overview of recent direct preference elicitation methods: these methods ask users to answer (ideally, a small number of) queries regarding their preferences and use this information to recommend a feasible decision that would be (approximately) optimal given those preferences. We argue for the importance of assessing numerical utilities rather than qualitative preferences and survey several utility elicitation techniques from
Generating diverse plans to handle unknown and partially known user preferences
- ARTIFICIAL INTELLIGENCE
, 2012
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PTIME: Personalized Assistance for Calendaring
"... In a world of electronic calendars, the prospect of intelligent, personalized time management assistance seems a plausible and desirable application of AI. PTIME (Personalized Time Management) is a learning cognitive assistant agent that helps users handle email meeting requests, reserve venues, and ..."
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Cited by 9 (1 self)
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In a world of electronic calendars, the prospect of intelligent, personalized time management assistance seems a plausible and desirable application of AI. PTIME (Personalized Time Management) is a learning cognitive assistant agent that helps users handle email meeting requests, reserve venues, and schedule events. PTIME is designed to unobtrusively learn scheduling preferences, adapting to its user over time. The agent allows its user to flexibly express requirements for new meetings, as they would to an assistant. It interfaces with commercial enterprise calendaring platforms, and it operates seamlessly with users who do not have PTIME. This article overviews the system design and describes the models and technical advances required to satisfy the competing needs of preference modeling and elicitation, constraint reasoning, and machine learning. We further report on a multifaceted evaluation of the perceived usefulness of the system.
Regret-based Optimal Recommendation Sets in Conversational Recommender Systems
, 2009
"... Current conversational recommender systems are unable to offer guarantees on the quality of their recommendations due to a lack of principled user utility models. We develop an approach to recommender systems that incorporates an explicit utility model into the recommendation process in adecision-th ..."
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Cited by 9 (4 self)
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Current conversational recommender systems are unable to offer guarantees on the quality of their recommendations due to a lack of principled user utility models. We develop an approach to recommender systems that incorporates an explicit utility model into the recommendation process in adecision-theoretically sound fashion. The system maintains explicit constraints on user utility based on preferences revealed by the user’s actions. We investigate a new decision criterion, setwise minimax regret (SMR), for constructing optimal recommendation sets: we develop algorithms for computing SMR, and prove that SMR determines choice sets for queries thataremyopicallyoptimal. This provides a natural basis for generating compound critiques in conversational recommender systems. Our simulation results suggest that this utility-theoretically sound approach to user modeling allows much more effective navigation of a product space than traditional approaches based on, for example, heuristic utility models and product similarity measures.
