1,237 research outputs found

    Online Reciprocal Recommendation with Theoretical Performance Guarantees

    Get PDF
    A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences of both users. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to those achieved by a clearvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines

    An LSH Index for Computing Kendall's Tau over Top-k Lists

    Full text link
    We consider the problem of similarity search within a set of top-k lists under the Kendall's Tau distance function. This distance describes how related two rankings are in terms of concordantly and discordantly ordered items. As top-k lists are usually very short compared to the global domain of possible items to be ranked, creating an inverted index to look up overlapping lists is possible but does not capture tight enough the similarity measure. In this work, we investigate locality sensitive hashing schemes for the Kendall's Tau distance and evaluate the proposed methods using two real-world datasets.Comment: 6 pages, 8 subfigures, presented in Seventeenth International Workshop on the Web and Databases (WebDB 2014) co-located with ACM SIGMOD201

    Axiomatic rationality and ecological rationality

    Get PDF

    Learning, monetary policy and asset prices

    Get PDF
    The dissertation examines several policy-related implications of relaxing the assumption that economic agents are guided by rational expectations. A first, introductory chapter presents the main technical issues related to adaptive learning. The second chapter studies the implications for monetary policy of positing that both the private sector and the central bank form their expectations through adaptive learning and that the central bank has private information on shocks to the economy but cannot credibly commit. The main finding of this chapter is that when agents learn adaptively a bias against activist policy arises. The following chapter focuses on large, non-linear models, where no unambiguous linear approximation eligible as perceived law of motion exists. Accordingly, there are heterogeneous expectations and the system converges to a misspecification equilibrium, affected by the communication strategies of the central bank. The main results are: (1) the heterogeneity of expectations persists even when a large number of observations are available; (2) the monetary policymaker has no incentive to be an inflation hawk; (3) partial transparency enhances welfare somewhat but full transparency does not. The final chapter adopts a model in which agents are fully informed and use Bayesian techniques to estimate the hidden states of the economy. The monetary policy stance is unobservable and state-independent, generating uncertainty among agents, who try to gauge it from inflation: a change in consumer prices that confirms beliefs reduces stock risk premia, while a change that contradicts beliefs drives the risk premia upward. This may generate a negative correlation between returns and inflation that explains the Fisher puzzle. The model is tested on US data. The econometric evidence suggests: (1) that a mimickingportfolio proxying for monetary policy uncertainty is a risk factor priced by financial markets; and (2) that conditioning on monetary uncertainty and fundamentals eliminates the Fisher puzzle

    Capturing Risk in Capital Budgeting

    Get PDF
    NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

    Bounded rationality and spatio-temporal pedestrian shopping behavior

    Get PDF
    corecore