Collaborative Filtering with Data Classification : A Combined Approach to Hotel Recommendation Systems

Recommendation systems aim to help users make decisions more efficiently. The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the "cold start" problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.

Reputation and Legitimacy : accreditation and rankings to assess organizations

A key perspective on reputation is that of assessment. Much of the communication literature focuses on the influence organizations have on impression formation. This chapter however suggests that in order to understand reputation assessment, it is also important to understand the related concept of legitimacy. It addresses two approaches to understanding reputation namely accreditation and ranking. Accreditation alludes to concepts of legitimacy in which firms may acquire credibility by meeting formalized standards of certification. Ranking deals with categorizing and rating organizational reputations so that they may be assessed relative to one another. The chapter explores the various ways in which the mechanisms of accreditation and ranking operate and the role of social actors in developing and applying them. Ranking systems that provide the mechanism for comparing organizations and assessing their relative value are also explored.

PaleoTools - extended software package for quantitative paleoenvironmental reconstructions

The Imbrie and Kipp transfer function method (IKM) and the modern analog technique (MAT) are accepted tools for quantitative paleoenvironmental reconstructions. However, no uncomplicated, flexible software has been available to apply these methods on modern computer devices. For this reason the software packages PaleoToolBox, MacTransfer, WinTransfer, MacMAT, and PanPlot have been developed. The PaleoToolBox package provides a flexible tool for the preprocessing of microfossil reference and downcore data as well as hydrographic reference parameters. It includes procedures to randomize the raw databases; to switch specific species in or out of the total species list; to establish individual ranking systems and their application on the reference and downcore databasessemi; and to convert the prepared databases into the file formats of IKM and MAT software for estimation of paleohydrographic parameters.

Information Discovery on Electronic Health Records Using Authority Flow Techniques

Background As the use of electronic health records (EHRs) becomes more widespread, so does the need to search and provide effective information discovery within them. Querying by keyword has emerged as one of the most effective paradigms for searching. Most work in this area is based on traditional Information Retrieval (IR) techniques, where each document is compared individually against the query. We compare the effectiveness of two fundamentally different techniques for keyword search of EHRs. Methods We built two ranking systems. The traditional BM25 system exploits the EHRs' content without regard to association among entities within. The Clinical ObjectRank (CO) system exploits the entities' associations in EHRs using an authority-flow algorithm to discover the most relevant entities. BM25 and CO were deployed on an EHR dataset of the cardiovascular division of Miami Children's Hospital. Using sequences of keywords as queries, sensitivity and specificity were measured by two physicians for a set of 11 queries related to congenital cardiac disease. Results Our pilot evaluation showed that CO outperforms BM25 in terms of sensitivity (65% vs. 38%) by 71% on average, while maintaining the specificity (64% vs. 61%). The evaluation was done by two physicians. Conclusions Authority-flow techniques can greatly improve the detection of relevant information in EHRs and hence deserve further study.

Étude de la médiane de permutations sous la distance de Kendall-Tau

La distance de Kendall-τ compte le nombre de paires en désaccord entre deux permuta- tions. La distance d’une permutation à un ensemble est simplement la somme des dis- tances entre cette permutation et les permutations de l’ensemble. À partir d’un ensemble donné de permutations, notre but est de trouver la permutation, appelée médiane, qui minimise cette distance à l’ensemble. Le problème de la médiane de permutations sous la distance de Kendall-τ, trouve son application en bio-informatique, en science politique, en télécommunication et en optimisation. Ce problème d’apparence simple est prouvé difficile à résoudre. Dans ce mémoire, nous présentons plusieurs approches pour résoudre le problème, pour trouver une bonne solution approximative, pour le séparer en classes caractéristiques, pour mieux com- prendre sa compléxité, pour réduire l’espace de recheche et pour accélérer les calculs. Nous présentons aussi, vers la fin du mémoire, une généralisation de ce problème et nous l’étudions avec ces mêmes approches. La majorité du travail de ce mémoire se situe dans les trois articles qui le composent et est complémenté par deux chapitres servant à les lier.

Étude de la médiane de permutations sous la distance de Kendall-Tau

La distance de Kendall-τ compte le nombre de paires en désaccord entre deux permuta- tions. La distance d’une permutation à un ensemble est simplement la somme des dis- tances entre cette permutation et les permutations de l’ensemble. À partir d’un ensemble donné de permutations, notre but est de trouver la permutation, appelée médiane, qui minimise cette distance à l’ensemble. Le problème de la médiane de permutations sous la distance de Kendall-τ, trouve son application en bio-informatique, en science politique, en télécommunication et en optimisation. Ce problème d’apparence simple est prouvé difficile à résoudre. Dans ce mémoire, nous présentons plusieurs approches pour résoudre le problème, pour trouver une bonne solution approximative, pour le séparer en classes caractéristiques, pour mieux com- prendre sa compléxité, pour réduire l’espace de recheche et pour accélérer les calculs. Nous présentons aussi, vers la fin du mémoire, une généralisation de ce problème et nous l’étudions avec ces mêmes approches. La majorité du travail de ce mémoire se situe dans les trois articles qui le composent et est complémenté par deux chapitres servant à les lier.

Tensor-based item recommendation using probabilistic ranking in social tagging systems

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.

Utilizing voting systems for ranking user tweets

Twitter is a very popular social network website that allows users to publish short posts called tweets. Users in Twitter can follow other users, called followees. A user can see the posts of his followees on his Twitter profile home page. An information overload problem arose, with the increase of the number of followees, related to the number of tweets available in the user page. Twitter, similar to other social network websites, attempts to elevate the tweets the user is expected to be interested in to increase overall user engagement. However, Twitter still uses the chronological order to rank the tweets. The tweets ranking problem was addressed in many current researches. A sub-problem of this problem is to rank the tweets for a single followee. In this paper we represent the tweets using several features and then we propose to use a weighted version of the famous voting system Borda-Count (BC) to combine several ranked lists into one. A gradient descent method and collaborative filtering method are employed to learn the optimal weights. We also employ the Baldwin voting system for blending features (or predictors). Finally we use the greedy feature selection algorithm to select the best combination of features to ensure the best results.

Systems for rapid ranking of environmental pollutants.

Mode of access: Internet.

Final report : comparative ranking of 0.1-10 MWe solar thermal electric power systems /

"Contract No. EG-77-C-01-4042."

Land management systems : BLM faces risks in completing the Automated Land and Mineral Record System : report to the Chairman and Ranking Minority Member, Subcommittee on Interior and Related Agencies, Committee on Appropriations, House of Representatives /

"B-271929"--P. 1.

Food stamp program : implementation of electronic benefit transfer systems : report to the ranking minority member, Committee on Agriculture, Nutrition, and Forestry, U.S. Senate /

"GAO-02-332."

Service bid comparisons by fuzzy ranking in open railway market timetabling

In an open railway access market, the Infrastructure Provider (IP), upon the receipts of service bids from the Train Service Providers (TSPs), assigns track access rights according to its own business objectives and the merits of the bids; and produces the train service timetable through negotiations. In practice, IP chooses to negotiate with the TSPs one by one in such a sequence that IP optimizes its objectives. The TSP bids are usually very complicated, containing a large number of parameters in different natures. It is a difficult task even for an expert to give a priority sequence for negotiations from the contents of the bids. This study proposes the application of fuzzy ranking method to compare and prioritize the TSP bids in order to produce a negotiation sequence. The results of this study allow investigations on the behaviors of the stakeholders in bid preparation and negotiation, as well as evaluation of service quality in the open railway market.

Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea

This paper describes the formalization and application of a methodology to evaluate the safety benefit of countermeasures in the face of uncertainty. To illustrate the methodology, 18 countermeasures for improving safety of at grade railroad crossings (AGRXs) in the Republic of Korea are considered. Akin to “stated preference” methods in travel survey research, the methodology applies random selection and laws of large numbers to derive accident modification factor (AMF) densities from expert opinions. In a full Bayesian analysis framework, the collective opinions in the form of AMF densities (data likelihood) are combined with prior knowledge (AMF density priors) for the 18 countermeasures to obtain ‘best’ estimates of AMFs (AMF posterior credible intervals). The countermeasures are then compared and recommended based on the largest safety returns with minimum risk (uncertainty). To the author's knowledge the complete methodology is new and has not previously been applied or reported in the literature. The results demonstrate that the methodology is able to discern anticipated safety benefit differences across candidate countermeasures. For the 18 at grade railroad crossings considered in this analysis, it was found that the top three performing countermeasures for reducing crashes are in-vehicle warning systems, obstacle detection systems, and constant warning time systems.