Improving package recommendations through query relaxation

Recommendation systems aim to identify items that are likely to be of interest to users. In many cases, users are interested in package recommendations as collections of items. For example, a dietitian may wish to derive a dietary plan as a collection of recipes that is nutritionally balanced, and a travel agent may want to produce a vacation package as a coordinated collection of travel and hotel reservations. Recent work has explored extending recommendation systems to support packages of items. These systems need to solve complex combinatorial problems, enforcing various properties and constraints defined on sets of items. Introducing constraints on packages makes recommendation queries harder to evaluate, but also harder to express: Queries that are under-specified produce too many answers, whereas queries that are over-specified frequently miss interesting solutions. In this paper, we study query relaxation techniques that target package recommendation systems. Our work offers three key insights: First, even when the original query result is not empty, relaxing constraints can produce preferable solutions. Second, a solution due to relaxation can only be preferred if it improves some property specified by the query. Third, relaxation should not treat all constraints as equals: some constraints are more important to the users than others. Our contributions are threefold: (a) we define the problem of deriving package recommendations through query relaxation, (b) we design and experimentally evaluate heuristics that relax query constraints to derive interesting packages, and (c) we present a crowd study that evaluates the sensitivity of real users to different kinds of constraints and demonstrates that query relaxation is a powerful tool in diversifying package recommendations

A national survey to determine the status of tests and measurements in the physical education program for boys at the high school level (cities of 50,000-100,000 population)

Thesis (Ed.M.)--Boston Universit

CFD simulation of a mixing-sensitive reaction in unbaffled vessels

Stirred tanks are widely used in the process industry, often to carry out complex chemical reactions. In many of such cases the perfect mixing hypothesis is not applicable for modelling purposes, and more detailed modelling approaches are required in order to accurately describe the reactor behaviour. In this work a fully predictive modelling approach, based on Computational Fluid Dynamics, is developed. Model predictions are compared with original experimental data obtained in un unbaffled stirred vessel with a parallel-competitive, mixing sensitive reaction scheme. Notably, satisfactory results are obtained at all injection rates with no recourse to micro-mixing model, thus confirming the major role played by macro-mixing in the investigated system

Mass transfer and hydrodynamic characteristics of a Long Draft Tube Self-ingesting Reactor (LDTSR) for gas-liquid-solid operations

Gas-liquid stirred vessels are widely employed to carry out chemical reactions involving a gas reagent and a liquid phase. The usual way for introducing the gas stream into the liquid phase is through suitable distributors placed below the impeller. An interesting alternative is that of using “self ingesting” vessels where the headspace gas phase is injected and dispersed into the vessel through suitable surface vortices. In this work the performance of a Long Draft Tube Self-ingesting Reactor dealing with gas-liquid-solid systems, is investigated. Preliminary experimental results on the effectiveness of this contactor for particle suspension and gas-liquid mass transfer performance in presence of solid particles are presented. It is found that the presence of low particle fractions causes a significant increase in the minimum speed required for vortex ingestion of the gas. Impeller pumping capacity and gas-liquid mass transfer coefficient are found to be affected by the presence of solid particles, though to a lesser extent than with other self-ingesting devices

Ranking Models for the Temporal Dimension of Text

Temporal features of text have been shown to improve clustering and organization of documents, text classification, visualization, and ranking. Temporal ranking models consider the temporal expressions found in text (e.g., “in 2021” or “last year”) as time units, rather than as keywords, to define a temporal relevance and improve ranking. This paper introduces a new class of ranking models called Temporal Metric Space Models (TMSM), based on a new domain for representing temporal information found in documents and queries, where each temporal expression is represented as a time interval. Furthermore, we introduce a new frequency-based baseline called Temporal BM25 (TBM25). We evaluate the effectiveness of each proposed metric against a purely textual baseline, as well as several variations of the metrics themselves, where we change the aggregate function, the time granularity and the combination weight. Our extensive experiments on five test collections show statistically significant improvements of TMSM and TBM25 over state-of-the-art temporal ranking models. Combining the temporal similarity scores with the text similarity scores always improves the results, when the combination weight is between 2% and 6% for the temporal scores. This is true also for test collections where only 5% of queries contain explicit temporal expressions