This chapter introduces a personalized ranking function as a means of offering Web information seekers with search results that satisfy their particular interests. It argues that users’ preferences can be accurately identified based on the semantic analysis of their previous searches and that learnt user preferences can be fruitfully employed for personalizing search results. In this respect, we introduce a ranking formula that encapsulates the user’s interests in the process of ordering retrieved results so as to meet the user’s needs. For carrying out our study we relied on a lexical ontology that encodes a number of concepts and their interrelations and which helps us determine the semantics of both the query keywords and the query matching pages. Based on the correlation between the query and document semantics, our model decides upon the ordering of search results so that these are personalized.
There has been previous work in personalizing Web search. One approach to personalization is to have users explicitly describe their general search interests, which are stored as personal profiles (Pazzani, Muramatsu, & Billsus, 1996). Personal profiles, specified explicitly by the users have also been used to personalize rankings, such as the PageRank algorithm (Aktas, Nacar, & Menczer, 2004; Jeh & Widom, 2003). There also exist many works on the automatic learning of a user’s preference based on the analysis of the user’s past clickthrough history (Chen & Sycara, 2004; Pretschner & Gauch, 1999, Sugiyama, Hatano, & Yoshikawa, 2004). Pretschner and Gauch (1999) for instance, describe how a user’s preference is identified based on the five most frequent topics in the user’s log data.
On the other hand, Chen and Sycara (2004) generate multiple TF-IDF vectors, each representing the user’s interests in one area. Sugiyama et al. (2004) employ collaborative filtering techniques for learning the user’s preference from both the pages the user visited and those visited by users with similar interests. Likewise, Teevan, Dumais, and Horvitz (2005) employ rich models of user interests, built from both search-related information and information about the documents a user has read, created, and/or e-mailed.
Key Terms in this Chapter
Search Engine: A search engine is an information retrieval system designed to help find information stored on a computer system, such as on the World Wide Web, inside a corporate or proprietary network, or in a personal computer.
PageRank: A link analysis algorithm which assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of “measuring” its relative importance within the set. The algorithm may be applied to any collection of entities with reciprocal quotations and references. The numerical weight that it assigns to any given element E is also called the PageRank of E and denoted by PR(E).
Open Directory: The Open Directory Project (ODP) is a multilingual open content directory of World Wide Web links that is constructed and maintained by a community of volunteer editors.
Topi cal Ontology: In both computer science and information science, an ontology is a data model that represents a set of concepts within a domain and the relationships between those concepts. It is used to reason about the objects within that domain.
Collaborative Filtering: The method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of collaborative filtering approach is that those who agreed in the past tend to agree again in the future.
WordNet: WordNet is a semantic lexicon for the English language. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. The purpose is twofold: to produce a combination of dictionary and thesaurus that is more intuitively usable, and to support automatic text analysis and artificial intelligence applications.
Search Personalization: Search results customization for specific interests is widely known as personalized search. Personalized search has a significant potential in providing users with information that greatly satisfies their particular search intentions.
Complete Chapter List
Detailed Table of Contents
Yin-Leng Theng, Schubert Foo, Dion Goh, Jin-Cheon Na
Leonardo Candela, Donatella Castelli, Pasquale Pagano
Mohammed Nasser Al-Suqri, Esther O.A. Fatuyi
Jian-hua Yeh, Shun-hong Sie, Chao-chen Chen
Juan C. Lavariega, Lorena G. Gomez, Martha Sordia-Salinas, David A. Garza-Salazar
George Pyrounakis, Mara Nikolaidou
Ian H. Witten, David Bainbridge
Yin-Leng Theng, Nyein Chan Lwin Lwin, Jin-Cheon Na, Schubert Foo, Dion Hoe-Lian Goh
Schubert Foo, Yin-Leng Theng, Dion Hoe-Lian Goh, Jin-Cheon Na
Fu Lee Wang, Christopher C. Yang
K. S. Chudamani, H. C. Nagarathna
Payam M. Barnaghi, Wei Wang, Jayan C. Kurian
Giovanni Semeraro, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops
Shiyan Ou, Christopher S.G. Khoo, Dion Hoe-Lian Goh
Wooil Kim, John H.L. Hansen
Irene Lourdi, Mara Nikolaidou
Neide Santos, Fernanda C.A. Campos, Regina M.M. Braga Villela
Svenja Hagenhoff, Björn Ortelbach, Lutz Seidenfaden
Stefano Paolozzi, Fernando Ferri, Patrizia Grifoni
Ana Kovacevic, Vladan Devedzic
Jin-Cheon Na, Tun Thura Thet, Dion Hoe-Lian Goh, Yin-Leng Theng, Schubert Foo
Dion Hoe-Lian Goh, Khasfariyati Razikin, Alton Y.K. Chua, Chei Sian Lee, Schubert Foo
Taha Osman, Dhavalkumar Thakker, Gerald Schaefer
Stephen Kimani, Emanuele Panizzi, Tiziana Catarci, Margerita Antona
Spyros Veronikis, Giannis Tsakonas, Christos Papatheodorou
Mila M. Ramos, Luz Marina Alvaré, Cecilia Ferreyra, Peter Shelton
Robert Neumayer, Andreas Rauber
Gerald Schaefer, Simon Ruszala
Cláudio de Souza Baptista, Ulrich Schiel
Nuria Lloret Romero, Margarita Cabrera Méndez, Alicia Sellés Carot, Lilia Fernandez Aquino
Rubén Béjar, J. Nogueras-Iso, Miguel Ángel Latre, Pedro Rafael Muro-Medrano, F. J. Zarazaga-Soria
O. Cantán Casbas, J. Nogueras-Iso, F. J. Zarazaga-Soria
Piedad Garrido Picazo, Jesús Tramullas Saz, Manuel Coll Villalta
Wan Ab. Kadir Wan Dollah, Diljit Singh
Frances L. Lightsom, Alan O. Allwardt
Stephan Strodl, Christoph Becker, Andreas Rauber
Thomas Lidy, Andreas Rauber
Leonardo Bermón-Angarita, Antonio Amescua-Seco, Maria Isabel Sánchez-Segura, Javier García-Guzmán
Kanwal Ameen, Muhammad Rafiq
Seungwon Yang, Barbara M. Wildemuth, Jeffrey P. Pomerantz, Sanghee Oh
Faisal Ahmad, Tamara Sumner, Holly Devaul
Yongqing Ma, Warwick Clegg, Ann O’Brien
Chang Chew-Hung, John G. Hedberg
Michael B. Twidale, David M. Nichols
Soh Whee Kheng Grace