다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Despite hospitality and tourism researchers’ recent attempts on examining different aspects of online word-of-mouth [WOM], its impact on hotel sales remains largely unknown in the existing literature. To fill this void, we conduct a study to empirically investigate the impact of online consumer-generated reviews on hotel room sales. Utilizing data collected from the largest travel website in China, we develop a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings. Our results indicate a significant relationship between online consumer reviews and business performance of hotels.

The impact of online user reviews on hotel room sales [Summary]

Social media competitive analysis and text mining: A case study in the pizza industry

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.

A literature review and classification of recommender systems research

The consistency measure is a vital basis for consensus models of group decision making using preference relations, and includes two subproblems: individual consistency measure and consensus measure. In the analytic hierarchy process (AHP), the decision makers express their preferences using judgement matrices (i.e., multiplicative preference relations). Also, the geometric consistency index is suggested to measure the individual consistency of judgement matrices, when using row geometric mean prioritization method (RGMM), one of the most extended AHP prioritization procedures. This paper further defines the consensus indexes to measure consensus degree among judgement matrices (or decision makers) for the AHP group decision making using RGMM. By using Chiclana et al.'s consensus framework, and by extending Xu and Wei's individual consistency improving method, we present two AHP consensus models under RGMM. Simulation experiments show that the proposed two consensus models can improve the consensus indexes of judgement matrices to help AHP decision makers reach consensus. Moreover, our proposal has two desired features: (1) in reaching consensus, the adjusted judgement matrix has a better individual consistency index (i.e., geometric consistency index) than the corresponding original judgement matrix; (2) this proposal satisfies the Pareto principle of social choice theory.

Consensus models for AHP group decision making under row geometric mean prioritization method

As on-line community activities have increased exponentially, the need for a group recommendation system has also become more and more imperative. Although, the traditional recommendation system has achieved great success in supporting individuals' purchasing decisions, it is not suitable for supporting group purchasing decisions because its input can neither include items' ratings given by groups, nor can it generate recommendations for groups. Therefore, this study proposes a novel group recommendation system to satisfy this demand. The system is designed based on the framework of collaborative filtering. Especially, we use genetic algorithm to predict the possible interactions among group members so that we can correctly estimate the rating that a group of members might give to an item. The experimental results show that the proposed system can give satisfactory and high quality group recommendations.

A group recommendation system with consideration of interactions among group members

This study proposed a framework which integrates subjective and objective information to generate recommendations for an active consumer.The proposed algorithm applied a fuzzy linguistic model.The proposed algorithms were evaluated by a real dataset.The proposed algorithm performed excellent with the traditional algorithms. This study proposes a novel collaborative filtering framework which integrates both subjective and objective information to generate recommendations for an active consumer. The proposed framework can solve the problem of sparsity and the cold-start problem which affect traditional CF algorithms. The fuzzy linguistic model, which is a more natural way for the consumer to present their preferences, is adopted within the proposed framework. Based on these concepts, two algorithms, a simple aggregated (SA) algorithm and aggregated subjective and objective users' viewpoint (ASOV) algorithm are developed. A series of experiments is performed, the results of which indicate that the proposed methodologies produce high-quality recommendations. Finally, the results confirm that the proposed algorithms perform better than the traditional method.

A fuzzy recommender system based on the integration of subjective preferences and objective information

The group ranking problem is used to construct coherent aggregate results from preference data provided by decision makers. Although there have been different input formats used to represent user preferences, they share a common weakness, that the input mode is static. In other words, users must provide all the preference data at one time. To overcome this weakness, we propose a framework which allows users to provide partial and/or incomplete preference data at multiple times. Since this is a complicated issue, we specifically focus on a particular aspect as a first attempt at this framework. Accordingly, we reexamine a variant of the group ranking problem, the maximum consensus mining problem, which will give the longest ranking lists of alternatives that agree with the majority and disagree only with the minority, under the dynamic input mode assumption. An algorithm is developed to determine the maximum consensus sequences from the users' partial ranking data. Finally, extensive experiments are carried out using synthetic data sets. The results indicate that the proposed method is computationally efficient, and can effectively identify consensus among all users.

An approach to group ranking decisions in a dynamic environment

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. This paper proposes an attention-based long short-term memory model to predict stock price movement and make trading strategies

Li-Chen Cheng

Papers

A group recommendation system with consideration of interactions among group members

A fuzzy recommender system based on the integration of subjective preferences and objective information

An approach to group ranking decisions in a dynamic environment

Applied attention-based LSTM neural networks in stock prediction

Mining maximum consensus sequences from group ranking data