Compact Markov-modulated models for multiclass trace fitting

Markov-modulated Poisson processes (MMPPs) are stochastic models for fitting empirical traces for simulation, workload characterization and queueing analysis purposes. In this paper, we develop the first counting process fitting algorithm for the marked MMPP (M3PP), a generalization of the MMPP for modeling traces with events of multiple types. We initially explain how to fit two-state M3PPs to empirical traces of counts. We then propose a novel form of composition, called interposition, which enables the approximate superposition of several two-state M3PPs without incurring into state space explosion. Compared to exact superposition, where the state space grows exponentially in the number of composed processes, in interposition the state space grows linearly in the number of composed M3PPs. Experimental results indicate that the proposed interposition methodology provides accurate results against artificial and real-world traces, with a significantly smaller state space than superposed processes

A Unifying Framework for the Approximate Solution of Closed Multiclass Queuing Networks

Queuing network models of modern computing systems must consider a large number of components (e.g., Web servers, DB servers, application servers, firewall, routers, networks) and hundreds of customers with very different resource requirements. The complexity of such models makes the application of exact solution techniques prohibitively expensive, motivating research on approximate methods. This paper proposes an interpolation-matching framework that allows a unified view of approximate solution techniques for closed product-form queuing networks. Depending upon the interpolating functional form and the matching populations selected, a large versatile family of new approximations can be generated. It is shown that all the known approximation strategies, including Linearizer, are instances of the interpolation-matching framework. Furthermore, a new approximation technique, based on a third-order polynomial, is obtained using the interpolation-matching framework. The new technique is shown to be more accurate than other known methods

Top-N recommendations on Unpopular Items with Contextual Knowledge

Traditional recommender systems provide recommendations of items to users; recently, some of them also consider the context related to predictions. In this paper we propose a technique that relies on classical recommendation algorithms and post-filters recommendations on the basis of contextual information available for them. Association rules are exploited to identify the most significant correlations among context and item characteristics. The mined rules are used to filter the predictions performed by traditional recommender systems to provide contextualized recommendations. Our experimental results show that the proposed approach allows improving the output of classical algorithms proposed in the literature, especially in the case of unpopular items

Towards Improved QUBO Formulations of IR Tasks for Quantum Annealers

In recent years the interest in applying Quantum Computing to Information Retrieval and Recommendation Systems task has increased and several papers have proposed formulations of relevant tasks that can be solved with quantum devices (community detection, feature selection etc.), usually focusing on Quantum Annealers (QA), a special purpose device able to solve combinatorial optimization problems. However, most research only focuses on the mathematical aspect of the formulation, without accounting for the underlying physical processes of the quantum device. Indeed, theoretical studies indicate that certain characteristics make a problem difficult to solve on QA, but it is not clear how to use this knowledge to inform the development of better problem formulations that are equivalent but easier to solve on QA. This work presents a preliminary study which approaches this issue with an empirical perspective. We consider several problems both general and related to IR and Recommendation tasks to assess whether we can identify characteristics of the problem formulation or the solution space that affect the effectiveness of QA. The results indicate interesting correlations and suggest that this is a promising area to investigate further

Workshop on Learning and Evaluating Recommendations with Impressions (LERI)

Recommender systems typically rely on past user interactions as the primary source of information for making predictions. However, although highly informative, past user interactions are strongly biased. Impressions, on the other hand, are a new source of information that indicate the items displayed on screen when the user interacted (or not) with them, and have the potential to impact the field of recommender systems in several ways. Early research on impressions was constrained by the limited availability of public datasets, but this is rapidly changing and, as a consequence, interest in impressions has increased. Impressions present new research questions and opportunities, but also bring new challenges. Several works propose to use impressions as part of recommender models in various ways and discuss their information content. Others explore their potential in off-policy-estimation and reinforcement learning. Overall, the interest of the community is growing, but efforts in this direction remain disconnected. Therefore, we believe that a workshop would be useful in bringing the community together

Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances

In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one limitation of VQAs is their reliance on fixed-structure circuits, which may not be taylored for specific problems or hardware configurations. A leading strategy to address this issue are Adaptative VQAs, which dynamically modify the circuit structure by adding and removing gates, and optimize their parameters during the training. Several Adaptative VQAs, based on heuristics such as circuit shallowness, entanglement capability and hardware compatibility, have already been proposed in the literature, but there is still lack of a systematic comparison between the different methods. In this paper, we aim to fill this gap by analyzing three Adaptative VQAs: Evolutionary Variational Quantum Eigensolver (EVQE), Variable Ansatz (VAns), already proposed in the literature, and Random Adapt-VQE (RA-VQE), a random approach we introduce as a baseline. In order to compare these algorithms to traditional VQAs, we also include the Quantum Approximate Optimization Algorithm (QAOA) in our analysis. We apply these algorithms to QUBO problems and study their performance by examining the quality of the solutions found and the computational times required. Additionally, we investigate how the choice of the hyperparameters can impact the overall performance of the algorithms, highlighting the importance of selecting an appropriate methodology for hyperparameter tuning. Our analysis sets benchmarks for Adaptative VQAs designed for near-term quantum devices and provides valuable insights to guide future research in this area

Feature Selection for Classification with QAOA

Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models. However, feature selection is a computationally expensive task with a solution space that grows combinatorically. In this work, we consider in particular a quadratic feature selection problem that can be tackled with the Quantum Approximate Optimization Algorithm (QAOA), already employed in combinatorial optimization. First we represent the feature selection problem with the QUBO formulation, which is then mapped to an Ising spin Hamiltonian. Then we apply QAOA with the goal of finding the ground state of this Hamiltonian, which corresponds to the optimal selection of features. In our experiments, we consider seven different real-world datasets with dimensionality up to 21 and run QAOA on both a quantum simulator and, for small datasets, the 7-qubit IBM (ibm-perth) quantum computer. We use the set of selected features to train a classification model and evaluate its accuracy. Our analysis shows that it is possible to tackle the feature selection problem with QAOA and that currently available quantum devices can be used effectively. Future studies could test a wider range of classification models as well as improve the effectiveness of QAOA by exploring better performing optimizers for its classical step

Does the structure of the QUBO problem affect the effectiveness of quantum annealing? An empirical perspective

In recent years there has been a significant interest in exploring the potential of Quantum Annealers (QA) as heuristic solvers of Quadratic Unconstrained Binary Optimization (QUBO) problems. Some problems are more difficult to solve on QA and understanding why is not straightforward, because an analytical study of the underlying physical system is intractable for large QUBO problems. This work consists in an empirical analysis of the features making a QUBO problem difficult to solve on QA, based on clusters of QUBO instances identified with Hierarchical Clustering. The analysis reveals correlations between specific values of the features and the ability of QA to solve effectively the instances. These initial results open new research opportunities to inform the development of new AI methods supporting quantum computation (e.g., for minor embedding or error mitigation) that are better tailored to the characteristics of the problem, as well as to develop better QUBO formulations for known problems in order to improve the quality of the solutions found by QA

Impressions in Recommender Systems: Present and Future

Impressions are a novel data source providing researchers and practitioners with more details about user interactions and their context. In particular, an impression contain the items shown on screen to users, alongside users' interactions toward such items. In recent years, interest in impressions has thrived, and more papers use impressions in recommender systems. Despite this, the literature does not contain a comprehensive review of the current topics and future directions. This work summarizes impressions in recommender systems under three perspectives: recommendation models, datasets with impressions, and evaluation methodologies. Then, we propose several future directions with an emphasis on novel approaches. This work is part of an ongoing review of impressions in recommender systems