A Preliminary Investigation of User Perception and Behavioral Intention for Different Review Types: Customers and Designers Perspective

Qazi, Atika; Raj, Ram Gopal; Tahir, Muhammad; Waheed, Mehwish; Khan, Saif Ur Rehman; Abraham, Ajith

doi:10.1155/2014/872929

Cited by 43 publications

(34 citation statements)

References 37 publications

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“…Several recent studies explore feedback fro m users regarding their preferences for certain opinion summarization styles and approaches. Most recently, Qazi et al [26] addressed a gap in existing studies examining the determination of useful opinion review types from customers and designers perspectives. Specifically, the researchers used the Technology Acceptance Model (TAM) as a lens to analyze users' perce ptions toward different opinion review types and online review systems.…”

Section: Previous Workmentioning

confidence: 99%

What Users Prefer and Why: A User Study on Effective Presentation Styles of Opinion Summarization

Yuan

Begany

et al. 2015

Human-Computer Interaction – INTERACT 2015

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Section: Previous Workmentioning

confidence: 99%

What Users Prefer and Why: A User Study on Effective Presentation Styles of Opinion Summarization

Yuan

Begany

et al. 2015

Human-Computer Interaction – INTERACT 2015

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“…On the other hand, other research directions, which are receiving increasing attention in recent years, include [10]: online learning to rank for quickly learning the best re-ranking of the top position of the original ranked list based on real-time user click feedback [11][42]; large-scale learning to rank which leverages both the learning theory and computational theory for ranking when facing large-scale training data [39][45] [50]; learning to rank for diversity aims to optimize not only for relevancy, but also for diversity (i.e., for minimum redundancy) by taking into account document similarity and ranking context [41][44]; and robust learning to rank optimizes the tradeoffs between model effectiveness and robustness for real-world retrieval scenarios [49].…”

Section: Svmmentioning

confidence: 99%

Learning Ranking Functions For Information Retrieval Using Layered Multi-Population Genetic Programming

Yeh

Lin²

2017

MJCS

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Ranking plays a key role in many applications, such as document retrieval, recommendation, question answering, and machine translation. In practice, a ranking function (or model) is exploited to determine the rank-order relations between objects, with respect to a particular criterion. In this paper, a layered multipopulation genetic programming based method, known as RankMGP, is proposed to learn ranking functions for document retrieval by incorporating various types of retrieval models into a singular one with high effectiveness. RankMGP represents a potential solution (i.e., a ranking function) as an individual in a population of genetic programming and aims to directly optimize information retrieval evaluation measures in the evolution process. Overall, RankMGP consists of a set of layers and a sequential workflow running through the layers. In one layer, multiple populations evolve independently to generate a set of the best individuals. When the evolution process is completed, a new training dataset is created using the best individuals and the input training set of the layer. Then, the populations in the next layer evolve with the new training dataset. In the final layer, the best individual is obtained as the output ranking function. The proposed method is evaluated using the LETOR datasets and is found to be superior to classical information retrieval models, such as Okapi BM25. It is also statistically competitive with the state-of-the-art methods, including Ranking SVM, ListNet, AdaRank and RankBoost.Keywords: learning to rank for information retrieval, ranking function, supervised learning, layered multipopulation genetic programming, LAGEP, LETOR INTRODUCTIONOne central problem of information retrieval (IR) is the issue of determining which documents are relevant to the user's information needs, and which are not (i.e., finding the potentially relevant documents) [3]. In practice, it is usually addressed as a ranking problem, whose goal is, according to the degree of relevance (or similarity) between each document and the user's query, to define a total order of documents that ranks relevant documents in higher positions on the retrieved list than irrelevant ones.Traditional IR models, including the Boolean model, the vector space model, and the probabilistic model, are developed based on the bag-of-words model. In short, a document is decomposed into keywords (i.e., index terms) and a ranking function (or retrieval function) is defined to associate a relevance degree with the document and query [3]. The aforementioned models are typically realized in an unsupervised manner and thus, the parameters of underlying ranking functions are usually tuned empirically. However, manual tuning suffers high costs and sometimes leads to over-fitting, especially when the functions are carefully tuned to fit particular needs [30].Nowadays, as increasingly many IR results are accompanied by relevance judgments (e.g., query and clickthrough logs), supervised learning-based methods, referred to as "learning t...

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“…Opinion mining and sentiment analysis techniques are used to detect and extract subjective information in text documents [33]. Using sentiment analysis, we can find the overall contextual polarity about any topic provided by the author.…”

Section: Introductionmentioning

confidence: 99%

Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

Mustafa

Nawaz

Lali

et al. 2017

MJCS

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Social media has become a platform of first choice where one can express his/her feelings with freedom. The sports and matches being played are also discussed on social media such as Twitter. In this article, efforts are made to investigate the feasibility of using collective knowledge obtained from microposts posted on Twitter to predict the winner of a Cricket match. For predictions, we use three different methods that depend on the total number of tweets before the game for each team, fans sentiments toward each team and fans score predictions on Twitter. By combining these three methods, we classify winning team prediction in a Cricket game before the start of game. Our results are promising enough to be used for winning team forecast. Furthermore, the effectiveness of supervised learning algorithms is evaluated where Support Vector Machine (SVM) has shown advantage over other classifiers.

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A Preliminary Investigation of User Perception and Behavioral Intention for Different Review Types: Customers and Designers Perspective

Cited by 43 publications

References 37 publications

What Users Prefer and Why: A User Study on Effective Presentation Styles of Opinion Summarization

What Users Prefer and Why: A User Study on Effective Presentation Styles of Opinion Summarization

Learning Ranking Functions For Information Retrieval Using Layered Multi-Population Genetic Programming

Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

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