In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, Relevance Ranking for Vertical Search Engines teaches readers how to manipulate ranking algorithms to achieve better results in real-world applications. This reference book for professionals covers concepts and theories from the fundamental to the advanced, such as relevance, query intention, location-based relevance ranking, and cross-property ranking. It covers the most recent developments in vertical search ranking applications, such as freshness-based relevance theory for new search applications, location-based relevance theory for local search applications, and cross-property ranking theory for applications involving multiple verticals.

Relevance Ranking for Vertical Search Engines

Computers & Technology

Long, Bo

CHANG YI

Computer Science

Morgan Kaufmann

Relational Data Clustering: Models, Algorithms, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems. After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering: <ul> <li>Clustering on bi-type heterogeneous relational data</li> <li>Multi-type heterogeneous relational data</li> <li>Homogeneous relational data clustering</li> <li>Clustering on the most general case of relational data</li> <li>Individual relational clustering framework</li> <li>Recent research on evolutionary clustering</li> </ul> This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.

Business & Money

Yu, Philip S.

Zhang, Zhongfei

Economics

Chapman and Hall/CRC

A culmination of the authors’ years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems.
 After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering:
 <ol>
 <li>Clustering on bi-type heterogeneous relational data</li>
 <li>Multi-type heterogeneous relational data </li>
 <li>Homogeneous relational data clustering</li>
 <li>Clustering on the most general case of relational data</li>
 <li>Individual relational clustering framework</li>
 <li>Recent research on evolutionary clustering</li>
 
</ol>
 This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.

Graph Neural Networks for Natural Language Processing: A Survey (Foundations and Trends(r) in Machine Learning)

Pei, Jian

Chen, Yu

Shen, Kai

Wu, Lingfei

Guo, Xiaojie

Gao, Hanning

Li, Shucheng

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