Constructive Preference Elicitation over Hybrid Combinatorial Spaces

Preference elicitation is the task of suggesting a highly preferred configuration to a decision maker. The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects. In its constructive variant, new objects are synthesized "from scratch" by maximizing an estimate of the user utility over a combinatorial (possibly infinite) space of candidates. In the constructive setting, most existing elicitation techniques fail because they rely on exhaustive enumeration of the candidates. A previous solution explicitly designed for constructive tasks comes with no formal performance guarantees, and can be very expensive in (or unapplicable to) problems with non-Boolean attributes. We propose the Choice Perceptron, a Perceptron-like algorithm for learning user preferences from set-wise choice feedback over constructive domains and hybrid Boolean-numeric feature spaces. We provide a theoretical analysis on the attained regret that holds for a large class of query selection strategies, and devise a heuristic strategy that aims at optimizing the regret in practice. Finally, we demonstrate its effectiveness by empirical evaluation against existing competitors on constructive scenarios of increasing complexity.Comment: AAAI 2018, computing methodologies, machine learning, learning paradigms, supervised learning, structured output

Decomposition Strategies for Constructive Preference Elicitation

We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned itera tively by interacting with the user. Previous work has shown that Coactive Learning is a suitable method for learning user preferences in constructive scenarios. In Coactive Learning the user provides feedback to the algorithm in the form of an improvement to a suggested configuration. When the problem involves many decision variables and constraints, this type of interaction poses a significant cognitive burden on the user. We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations. This has the clear advantage of drastically reducing the user cognitive load. Additionally, part-wise inference can be (up to exponentially) less computationally demanding than inference over full configurations. We discuss the theoretical implications of working with parts and present promising empirical results on one synthetic and two realistic constructive problems.Comment: Accepted at the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18

Price Effects in the Short and in the Long Run

We study a broad class of dynamic consumer problems and characterize the short and long-run response of the demand for a good to a permanent increase in its market price. Such response can be non-monotonic over time, and the short and long-run price-elasticity of demand may have opposite sign. This is a testable prediction and can arise even in the absence of income effects. Our results are robust to a variety of settings that are commonly used in the economic literature, and have relevant policy implications. We provide illustrative applications to models of human capital and labor supply, addiction, habit and taste formation, health capital, and renewable resources

Substitution Effects in Intertemporal Problems

We consider a broad class of intertemporal economic problems and we characterize the short and long-run response of the demand for a good to a permanent increase in its market price. Depending on the interplay between self-productivity and time discounting, we show that dynamic substitution effects can generate price elasticities of opposite sign in the short and in the long run

Recreational cannabis reduces rapes and thefts: Evidence from a quasi-experiment

An argument against the legalization of the cannabis market is that such a policy would increase crime. Exploiting the recent staggered legalization enacted by the states of Washington (end of 2012) and Oregon (end of 2014) we show, combining difference-in-differences and spatial regression discontinuity designs, that recreational cannabis caused a significant reduction of rapes and thefts on the Washington side of the border in 2013-2014 relative to the Oregon side and relative to the pre-legalization years 2010-2012

Classification and Resolution of Non-Sentential Utterances in Dialogue

This article addresses the problems of classification and resolution of non-sentential utterances (NSUs) in dialogue. NSUs are utterances that do not have a complete sentential form but convey a full clausal meaning given the conversational context, such as “To the contrary!” or “How much?”. The presented approach builds upon the work of Fernández, Ginzburg, and Lappin (2007), who provide a taxonomy of NSUs divided in 15 classes along with a small annotated corpus extracted from dialogue transcripts. The main part of this article focuses on the automatic classification of NSUs according to these classes. We show that a combination of novel linguistic features and active learning techniques yields a significant improvement in the classification accuracy over the state-of-the-art, and is able to mitigate the scarcity of labelled data. Based on this classifier, the article also presents a novel approach for the semantic resolution of NSUs in context using probabilistic rules

Constructive Preference Elicitation

When faced with large or complex decision problems, human decision makers (DM) can make costly mistakes, due to inherent limitations of their memory, attention, and knowledge. Preference elicitation tools assist the decision maker in overcoming these limitations. They do so by interactively learning the DM’s preferences through appropriately chosen queries and suggesting high-quality outcomes based on the preference estimates. Most state-of-the-art techniques, however, fail in constructive settings, where the goal is to synthesize a custom or entirely novel configuration rather than choosing the best option among a given set of candidates. Many wide-spread problems are constructive in nature: customizing composite goods such as cars and computers, bundling products, recommending touristic travel plans, designing apartments, buildings, or urban layouts, etc. In these settings, the full set of outcomes is humongous and can not be explicitly enumerated, and the solution must be synthesized via constrained optimization. In this article, we describe recent approaches especially designed for constructive problems, outlining the underlying ideas and their differences as well as their limitations. In presenting them, we especially focus on novel issues that the constructive setting brings forth, such as how to deal with sparsity of the DM’s preferences, how to properly frame the interaction, and how to achieve efficient synthesis of custom instances

Coactive Learning Algorithms for Constructive Preference Elicitation

Preference-based decision problems often involve choosing one among a large set of options, making common tasks like buying a car or a domestic appliance very challenging for a customer to handle on her own. This is especially true when buying online, where the amount of available options is humongous, and expert advice is yet limited. Recommender systems have become essential computational tools for aiding users in this endeavor. Recommender systems represent one of the most successful applications of artificial intelligence. In the last decades, several recommendation approaches have been proposed for different types of applications, from assisted browsing of product catalogs to personalization of results in search engines. Depending on the application, the job of the recommender system may be to recommend a satisfying option for the given context, as in finding the next best song to play, as opposed to helping the user in finding an optimal instance, e.g. when looking for an apartment. The former is generally handled by data-driven approaches, such as collaborative filtering and contextual bandits, while in the latter case data is usually scarce, making it necessary to employ specialized algorithms for preference elicitation. Preference elicitation algorithms interactively build a utility model of the user preferences and then recommend the instances with the highest utility. Preference elicitation is especially effective when recommending infrequently purchased items, such as professional work tools, electronic devices and other products that can be explicitly stored e.g. in the database of an e-commerce website. Standard preference elicitation algorithms, however, struggle when the options are so numerous that cannot even be explicitly enumerated, and instead need to be represented implicitly as a collection of variables and constraints. Indeed, when a customer wants to configure a product by putting several components together, e.g. for a custom personal computer, the option space is combinatorial and grows exponentially with the number of components, making it impractical to store every single feasible combination explicitly. This is an example of constructive decision problem, in which an object has to be synthesized on the basis of the preferences of the customer and the constraints over the configuration domain. Constructive problems such as product configuration have traditionally been addressed by specialized configurator systems, which guide the user through the configuration process component by component and check whether the user choices are consistent with the set of feasibility constraints. Over the years, however, the limitations of product configurators for mass customization have become apparent. With the growing scale of configuration problems, product configurators have become more difficult for non-experts to use and ultimately do not provide relief against the "mass confusion" caused by the sheer amount of choice. Research in this field has progressively been integrating recommendation technologies into configuration systems, in order to make them more flexible and easy to use. Preference elicitation in product configuration has been attempted as well but still remains a challenge. We propose a generic framework for preference elicitation in constructive domains, that is able to scale to large combinatorial problems better than existing techniques. Our constructive preference elicitation framework is based on online structured prediction, a machine learning technique that deals with sequential decision problems over structured objects. By combining online structured prediction and state-of-the-art constraint solvers we can efficiently learn user utility models and make increasingly better recommendations for complex preference-based constructive problems such as product configuration. In particular, we favor the use of coactive learning, an online structured prediction framework for preference learning. Coactive learning is particularly well suited for constructive preference elicitation as it may be seen as a cooperation between the user and the system. The user and the systems interact through "coactive" feedback: after each recommendation, the user provides a modification that makes it slightly better for her preferences. This type of feedback is very flexible and can be acquired both explicitly and implicitly from the user actions. Coactive learning also comes with theoretical convergence guarantees and a set of ready-made extensions for many related problems such as learning in a multi-user setting and learning with approximate constraint solvers. In this thesis we detail our coactive learning approach to constructive preference elicitation, and propose extensions for scaling up to very large constructive problems and personalizing the utility model. We also applied our framework to two important classes of constructive preference elicitation problems, namely layout synthesis and product bundling. The former is a design process for arranging objects into a given space, while the latter is a kind of product configuration problem in which the object to configure is a package of different products and services. Within the product bundling application, we also performed an extensive validation involving real participants, which highlights the practical benefits of our approach

Constructive Preference Elicitation

When faced with large or complex decision problems, human decision makers (DM) can make costly mistakes, due to inherent limitations of their memory, attention and knowledge. Preference elicitation tools assist the decision maker in overcoming these limitations. They do so by interactively learning the DM's preferences through appropriately chosen queries and suggesting high-quality outcomes based on the preference estimates. Most state-of-the-art techniques, however, fail in constructive settings, where the goal is to synthesize a custom or entirely novel configuration rather than choosing the best option among a given set of candidates. Many wide-spread problems are constructive in nature: customizing composite goods such as cars and computers, bundling products, recommending touristic travel plans, designing apartments, buildings or urban layouts, etc. In these settings, the full set of outcomes is humongous and can not be explicitly enumerated, and the solution must be synthesized via constrained optimization. In this paper we describe recent approaches especially designed for constructive problems, outlining the underlying ideas and their differences as well as their limitations. In presenting them we especially focus on novel issues that the constructive setting brings forth, such as how to deal with sparsity of the DM's preferences, how to properly frame the interaction, and how to achieve efficient synthesis of custom instances.status: publishe