Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.

/pdf/a-taxonomy-of-web-search-1c1wfauugs.pdf

A taxonomy of web search

Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100p recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

/pdf/content-based-multimedia-information-retrieval-state-of-the-41z647ll8w.pdf

Content-based multimedia information retrieval: State of the art and challenges

Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lexical taxonomy and information content from a corpus. To investigate how information sources could be used effectively, a variety of strategies for using various possible information sources are implemented. A new measure is then proposed which combines information sources nonlinearly. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed measure significantly outperforms traditional similarity measures.

An approach for measuring semantic similarity between words using multiple information sources

A system is presented providing content to a plurality of handheld devices (including musical selections). The devices can access a server over the Internet via a WIFI or other similar wireless interconnection and can download songs requested by a user from the server or from other users using, e.g., a P2P protocol. All downloads are governed by applicable DRM rules. Content and playlists may also be pushed by a server from other sources and means including, e.g., podcasting, based on predetermined rules, favorite preferences of users, and other criteria.

Audio visual player apparatus and system and method of content distribution using the same

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. - Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

Recommender Systems: The Textbook

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of "Web2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the "cold-start problem" common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system.

/pdf/automatic-generation-of-social-tags-for-music-recommendation-3v226se3gf.pdf

Automatic Generation of Social Tags for Music Recommendation

Users can browse a repository of search results obtained from a search engine by mounting a virtual file system, for example, on a network server over a network. The virtual file system contains a hierarchy of categories and is associated with a document repository. Consequently, although documents could be located anywhere, documents indexed by the virtual file system are accessed by users in the original document locations. Accordingly, all changes made by a user are made to the original document rather than to a copy of the document. Therefore, there is no need to upload a copy of the document to the original file location. The search engine can be associated with the virtual file system so that the search engine recognizes the changed document immediately.

Method and apparatus for browsing search results via a virtual file system

Most current automatic hypertext generation systems rely on term repetition to calculate the relatedness of two documents. There are well-recognized problems with such approaches, most notably, they are vulnerable to the linguistic effects of synonymy (many words for the same concept) and polysemy (many concepts for the same word). I propose a novel method for automatic hypertext generation that is based on a technique called lexical chaining, a method for discovering sets of related words in a text. I will also present the results of an empirical study designed to test this method in the context of a question answering task from a database of newspaper articles.

/pdf/automated-link-generation-can-we-do-better-than-term-50yb71sj2p.pdf

Automated link generation: can we do better than term repetition?

Automated Link Generation: Can we do Better than Term Repetition?

A method for searching and resource discovery in a distributed enterprise (DE), the method including: generating a first classifier for a first repository in the DE; generating a second classifier for a second repository in the DE, where the second classifier has a vector element identifying a location of the second repository; submitting a copy of the second classifier to a web server of the first repository; obtaining a resource at the first repository after submitting the copy of the second classifier; matching the resource to the copy of a second classifier; sending a copy of the resource to the second repository using the vector element; and storing the copy of the resource in the second repository.

Method and apparatus for searching and resource discovery in a distributed enterprise system

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