Online Reciprocal Recommendation with Theoretical Performance Guarantees

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

The price stabilization effects of the EU entry price scheme for fruits and vegetables

The paper assesses the stabilization effects of the EU import regime for fresh fruit and vegetables based on the entry price system. The analysis is carried out on the EU prices of tomatoes and lemons and those of imports from some of the main competing countries on the EU domestic markets: Morocco, Argentina and Turkey. It is based on the estimation of a threshold vector autoregressive econometric model that is shown capable of taking the workings of the import regime into account. The model shows that prices behave differently when import prices are above/below the trigger entry price. This paper allowed to highlight the cases for which the isolation effect of EPS seems reached and the resulting stabilization effects

On similarity prediction and pairwise clustering

We consider the problem of clustering a finite set of items from pairwise similarity information. Unlike what is done in the literature on this subject, we do so in a passive learning setting, and with no specific constraints on the cluster shapes other than their size. We investigate the problem in different settings: i. an online setting, where we provide a tight characterization of the prediction complexity in the mistake bound model, and ii. a standard stochastic batch setting, where we give tight upper and lower bounds on the achievable generalization error. Prediction performance is measured both in terms of the ability to recover the similarity function encoding the hidden clustering and in terms of how well we classify each item within the set. The proposed algorithms are time efficient

The price stabilization effects of the EU entry price scheme for fruits and vegetables

The paper assesses the stabilization effects of the EU import regime for fresh fruit and vegetables based on the entry price system. The analysis is carried out on the EU prices of tomatoes and lemons and those of imports from some of the main competing countries on the EU domestic markets: Morocco, Argentina and Turkey. It is based on the estimation of a threshold vector autoregressive econometric model that is shown capable of taking the workings of the import regime into account. The model shows that prices behave differently when import prices are above/below the trigger entry price. This paper allowed to highlight the cases for which the isolation effect of EPS seems reached and the resulting stabilization effects.Fruit and vegetables; Entry price system; stabilisation effects; TVAR

Correlation Clustering with Adaptive Similarity Queries

In correlation clustering, we are given $n$ objects together with a binary similarity score between each pair of them. The goal is to partition the objects into clusters so to minimise the disagreements with the scores. In this work we investigate correlation clustering as an active learning problem: each similarity score can be learned by making a query, and the goal is to minimise both the disagreements and the total number of queries. On the one hand, we describe simple active learning algorithms, which provably achieve an almost optimal trade-off while giving cluster recovery guarantees, and we test them on different datasets. On the other hand, we prove information-theoretical bounds on the number of queries necessary to guarantee a prescribed disagreement bound. These results give a rich characterization of the trade-off between queries and clustering error

Fisher Metric, Geometric Entanglement and Spin Networks

Starting from recent results on the geometric formulation of quantum mechanics, we propose a new information geometric characterization of entanglement for spin network states in the context of quantum gravity. For the simple case of a single-link fixed graph (Wilson line), we detail the construction of a Riemannian Fisher metric tensor and a symplectic structure on the graph Hilbert space, showing how these encode the whole information about separability and entanglement. In particular, the Fisher metric defines an entanglement monotone which provides a notion of distance among states in the Hilbert space. In the maximally entangled gauge-invariant case, the entanglement monotone is proportional to a power of the area of the surface dual to the link thus supporting a connection between entanglement and the (simplicial) geometric properties of spin network states. We further extend such analysis to the study of non-local correlations between two non-adjacent regions of a generic spin network graph characterized by the bipartite unfolding of an Intertwiner state. Our analysis confirms the interpretation of spin network bonds as a result of entanglement and to regard the same spin network graph as an information graph, whose connectivity encodes, both at the local and non-local level, the quantum correlations among its parts. This gives a further connection between entanglement and geometry.Comment: 29 pages, 3 figures, revised version accepted for publicatio