Dual Preference Distribution Learning for Item Recommendation

Recommender systems can automatically recommend users with items that they probably like. The goal of them is to model the user-item interaction by effectively representing the users and items. Existing methods have primarily learned the user's preferences and item's features with vectorized embeddings, and modeled the user's general preferences to items by the interaction of them. In fact, users have their specific preferences to item attributes and different preferences are usually related. Therefore, exploring the fine-grained preferences as well as modeling the relationships among user's different preferences could improve the recommendation performance. Toward this end, we propose a dual preference distribution learning framework (DUPLE), which aims to jointly learn a general preference distribution and a specific preference distribution for a given user, where the former corresponds to the user's general preference to items and the latter refers to the user's specific preference to item attributes. Notably, the mean vector of each Gaussian distribution can capture the user's preferences, and the covariance matrix can learn their relationship. Moreover, we can summarize a preferred attribute profile for each user, depicting his/her preferred item attributes. We then can provide the explanation for each recommended item by checking the overlap between its attributes and the user's preferred attribute profile. Extensive quantitative and qualitative experiments on six public datasets demonstrate the effectiveness and explainability of the DUPLE method.Comment: 23 pages, 7 figures. This manuscript has been accepted by ACM Transactions on Information System

Estimation of monetary policy preferences in a forward-looking model: a Bayesian approach. NBB Working Papers No. 129, 13 March 2008

In this paper, we adopt a Bayesian approach to estimating monetary policy preference parameters in a general equilibrium framework. We start out from the model presented by Smets and Wouters (2003) for the euro area, where, in the original set-up, monetary policy behaviour is described by an empirical rule. We abandon this way of representing monetary policy behaviour and instead assume that monetary policy authorities optimise an intertemporal quadratic loss function under commitment. We consider two alternative specifications for the loss function. The first specification includes inflation, the output gap and difference in the interest rate as target variables. The second loss function includes an additional wage inflation target. The weights assigned to the target variables in the loss functions, i.e. the preferences of monetary policy, are estimated jointly with the structural parameters in the model. The results imply that inflation variability remains the main concern of optimal monetary policy. In addition, interest rate smoothing and the output gap appear to be important target variables as well, albeit to a lesser extent. Comparing the marginal likelihood of the original Smets and Wouters (2003) model to our specification with optimal monetary policy indicates that the latter performs only slightly worse. Since we are faced with the time-inconsistency problem under commitment, we initialise our estimates by considering a pre-sample period of 40 quarters. This enables an empirical approach to the timeless perspective framework

Estimation of monetary policy preferences in a forward-looking model : a Bayesian approach

In this paper we adopt a Bayesian approach towards the estimation of the monetary policy preference parameters in a general equilibrium framework. We start from the model presented by Smets and Wouters (2003) for the euro area where, in the original set up, monetary policy behaviour is described by an empirical Taylor rule. We abandon this way of representing monetary policy behaviour and assume, instead, that monetary policy authorities optimize an intertemporal quadratic loss function under commitment. We consider two alternative specifications for the loss function. The first specification includes inflation, output gap and difference in the interest rate as target variables. The second loss function includes an additional wage inflation target. The weights assigned to the target variables in the loss functions, i.e. the preferences of monetary policy, are estimated jointly with the structural parameters in the model. The results imply that inflation variability remains the main concern of optimal monetary policy. In addition, interest rate smoothing and the output gap appear to be, to a lesser extent, important target variables as well. Comparing the marginal likelihood of the original Smets and Wouters (2003) model to our specification with optimal monetary policy indicates that the latter performs only slightly worse. Since we are faced with the time-inconsistency problem under commitment, we initialize our estimates by considering a presample period of 40 quarters. This allows us to approach, empirically, the timeless perspective framework.optimal monetary policy, commitment, central bank preferences, euro area monetary policy

Investment risk preferences of decision makers acting on behalf of German charitable trusts

This research programme investigates the subjective utility of monetary outcomes and applies the existing knowledge base regarding the quantification and description of risk preferences to German charitable trusts. Results are discussed on the basis of Expected Utility Theory (EUT) and Prospect Theory (PT) with a focus on the “Fourfold Pattern (4FP)” of PT. The description of risk preferences of trusts enables investors, advisors and portfolio managers to optimise their investment strategies for this specific target group disposing of an estimated asset base of about € 100bn. The subjects of this study, German charitable trusts, are restricted in their investment decisions by a given legal framework and therefore prone to deviate in their preferences from the subjects that have been examined in prior academic studies. The thesis aims at filling this research gap by applying the knowledge base of decision theory to German charitable trusts using an original set of representative data which was generated as part of this study. Firstly, regarding the general investment risk preferences of trusts, the study finds risk aversion predominating in the domain of gains and observes loss aversion, both analogous to prior research on private individuals. The PT pattern of risk-seeking behaviour for losses can only partly be asserted. In contrast to PT, no evidence is found for the subjective overweighting of small probabilities. Secondly, the study identifies and discusses characteristics of trusts which are associated with risk preferences: Equity investments, expected external growth of assets, age of the investment decision makers, type of donor and involvement of the donor in investment decisions.As a contribution to decision theory, the author proposes a utility function representing the preferences of trusts based on decision theoretical backgrounds. As a contribution to practical investment implications, the author proposes to redefine the question of “safe investments” and to focus on distributable yields generated by a higher equity portion in trust portfolios

A Component-oriented Framework for Autonomous Agents

The design of a complex system warrants a compositional methodology, i.e., composing simple components to obtain a larger system that exhibits their collective behavior in a meaningful way. We propose an automaton-based paradigm for compositional design of such systems where an action is accompanied by one or more preferences. At run-time, these preferences provide a natural fallback mechanism for the component, while at design-time they can be used to reason about the behavior of the component in an uncertain physical world. Using structures that tell us how to compose preferences and actions, we can compose formal representations of individual components or agents to obtain a representation of the composed system. We extend Linear Temporal Logic with two unary connectives that reflect the compositional structure of the actions, and show how it can be used to diagnose undesired behavior by tracing the falsification of a specification back to one or more culpable components