Описание: Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining presents novel algorithms for academic search, recommendation and association rule mining that have been developed and optimized for different commercial as well as academic purpose systems. Along with the design and implementation of algorithms, a major part of the work presented in the book involves the development of new systems both for commercial as well as for academic use. In the first part of the book the author introduces a novel hierarchical heuristic scheme for re-ranking academic publications retrieved from standard digital libraries. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper’s index terms with each other. In order to evaluate the performance of the introduced algorithms, a meta-search engine has been designed and developed that submits user queries to standard digital repositories of academic publications and re-ranks the top-n results using the introduced hierarchical heuristic scheme. In the second part of the book the design of novel recommendation algorithms with application in different types of e-commerce systems are described. The newly introduced algorithms are a part of a developed Movie Recommendation system, the first such system to be commercially deployed in Greece by a major Triple Play services provider. The initial version of the system uses a novel hybrid recommender (user, item and content based) and provides daily recommendations to all active subscribers of the provider (currently more than 30,000). The recommenders that we are presenting are hybrid by nature, using an ensemble configuration of different content, user as well as item-based recommenders in order to provide more accurate recommendation results.The final part of the book presents the design of a quantitative association rule mining algorithm. Quantitative association rules refer to a special type of association rules of the form that antecedent implies consequent consisting of a set of numerical or quantitative attributes. The introduced mining algorithm processes a specific number of user histories in order to generate a set of association rules with a minimally required support and confidence value. The generated rules show strong relationships that exist between the consequent and the antecedent of each rule, representing different items that have been consumed at specific price levels. This research book will be of appeal to researchers, graduate students, professionals, engineers and computer programmers.
Автор: Hongzhi Yin; Bin Cui Название: Spatio-Temporal Recommendation in Social Media ISBN: 9811007470 ISBN-13(EAN): 9789811007477 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media.
Описание: Presents a unique methodology to view science, technology and innovation (STI) in developing countries. The study provides a set of cases studies drawn from a diverse range of experiences across the Ugandan private sector and offers concrete policy recommendations on how to support broader development of STI in Uganda.
Автор: Xu Chen, Yongfeng Zhang Название: Explainable Recommendation: A Survey and New Perspectives ISBN: 1680836587 ISBN-13(EAN): 9781680836585 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 9841.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a comprehensive review of explainable recommendation research. The authors first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W. They then conduct a comprehensive survey of explainable recommendation.
Описание: This book constitutes refereed proceedings of the First International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2020, held in April, 2020.
Автор: Shah Chirag, White Ryen W. Название: Task Intelligence for Search and Recommendation ISBN: 1636391516 ISBN-13(EAN): 9781636391519 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12751.00 р. Наличие на складе: Нет в наличии.
Описание: While great strides have been made in the field of search and recommendation, there are still challenges and opportunities to address information access issues that involve solving tasks and accomplishing goals for a wide variety of users. Specifically, we lack intelligent systems that can detect not only the request an individual is making (what), but also understand and utilize the intention (why) and strategies (how) while providing information and enabling task completion. Many scholars in the fields of information retrieval, recommender systems, productivity (especially in task management and time management), and artificial intelligence have recognized the importance of extracting and understanding people's tasks and the intentions behind performing those tasks in order to serve them better. However, we are still struggling to support them in task completion, e.g., in search and assistance, and it has been challenging to move beyond single-query or single-turn interactions. The proliferation of intelligent agents has unlocked new modalities for interacting with information, but these agents will need to be able to work understanding current and future contexts and assist users at task level. This book will focus on task intelligence in the context of search and recommendation. Chapter 1 introduces readers to the issues of detecting, understanding, and using task and task-related information in an information episode (with or without active searching). This is followed by presenting several prominent ideas and frameworks about how tasks are conceptualized and represented in Chapter 2. In Chapter 3, the narrative moves to showing how task type relates to user behaviors and search intentions. A task can be explicitly expressed in some cases, such as in a to-do application, but often it is unexpressed. Chapter 4 covers these two scenarios with several related works and case studies. Chapter 5 shows how task knowledge and task models can contribute to addressing emerging retrieval and recommendation problems. Chapter 6 covers evaluation methodologies and metrics for task-based systems, with relevant case studies to demonstrate their uses. Finally, the book concludes in Chapter 7, with ideas for future directions in this important research area.
Автор: ?zg?r Ulusoy; Abdullah Uz Tansel; Erol Arkun Название: Recommendation and Search in Social Networks ISBN: 3319143786 ISBN-13(EAN): 9783319143781 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This edited volume offers a clear in-depth overview of research covering a variety of issues in social search and recommendation systems.
Описание: This book systematically introduces Point-of-interest (POI) recommendations in Location-based Social Networks (LBSNs). Lastly, the book discusses future research directions in this area. This book is intended for professionals involved in POI recommendation and graduate students working on problems related to location-based services.
Автор: McClintock Maxine Название: Letters of Recommendation ISBN: 193782800X ISBN-13(EAN): 9781937828004 Издательство: Неизвестно Цена: 3442.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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