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Recommendations | Recommender Systems

http://recommender-systems.org/recommendations

Dynamically adding hyperlinks is often used for personalization and is the only approach that will be considered here. Recommender systems can present their recommendations in other ways however. Amazon.com for example, also delivers recommendations through email. Another approach is to display the average rating of an item from people who are correlated with the user. Several factors can be considered in determining which documents should be suggested to the user:. Between a document and the user profile.

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Latent Semantic Indexing | Recommender Systems

http://recommender-systems.org/latent-semantic-indexing

LSI) is an extension of the vector space model. Note that Latent semantic indexing does not attempt to interpret the meaning of the factors but merely uses them to represent documents and vectors. A mathematical description of the LSI method is given below. Schematic of the matrix. From the complete collection of documents a term-document matrix X. Is formed with t. Rows (one for every term that appears in the set of documents) and d. Columns (one for every document in the set). A SVD of matrix A. The do...

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Content-based Filtering | Recommender Systems

http://recommender-systems.org/content-based-filtering

Content-based filtering, also referred to as cognitive filtering, recommends items based on a comparison between the content of the items and a user profile. The content of each item is represented as a set of descriptors or terms, typically the words that occur in a document. The user profile is represented with the same terms and built up by analyzing the content of items which have been seen by the user. And latent semantic indexing. And the Bayesian classifier. Are among the learning techniques.

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User Profile | Recommender Systems

http://recommender-systems.org/user-profile

The implementation of an information filtering system requires the creation of a user model, and subsequently a user profile. User modeling is defined as follows:. User modeling is a discipline that deals with both how information about the user can be acquired and used by an automated system. The description of what information is of interest to a user is commonly referred to as a user profile. Potential types of implicit feedback. Saves item to personal storage. Cites or otherwise refers to item.

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Mass Customization | Recommender Systems

http://recommender-systems.org/mass-customization

Recommender systems are used by e-commerce sites to suggest products and information about these products to customers. This enhances e-commerce sales in three ways:. Many people often browse through a web site without ever purchasing anything. By helping people find the products they are interested in and provide relevant product information, recommender systems can turn visitors into buyers. Click here to cancel reply. Mail (will not be published) (required). Notify me of follow-up comments by email.

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Vector Space Model | Recommender Systems

http://recommender-systems.org/vector-space-model

A representation that is often used for text documents is the vector space model. In the vector space model a document D. Is represented as an m. Dimensional vector, where each dimension corresponds to a distinct term and m. Is the total number of terms used in the collection of documents. The document vector is written as, where. Is the weight of term. That indicates its importance. If document D. Does not contain term. Scheme is called the term frequency. The number of occurrences of term. Appears in a...

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Text Mining | Recommender Systems

http://recommender-systems.org/text-mining

Text mining attempts to discover knowledge from text documents. Term extraction. Is usually the first step in a text mining process. Once the terms are found, several other text mining techniques can be used to enhance a content-based filtering. System. Two of these text mining techniques are document clustering and using thesauri. Representation. Because the vector lengths are much shorter in the LSI space it takes less time to calculate the similarity between two documents. Click here to cancel reply.

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Website Data Mining | Recommender Systems

http://recommender-systems.org/website-data-mining

Collaborative filtering systems that are based on user feedback have two limitations. First, they rely heavily on explicit feedback which has several drawbacks. Second, since the user models are static, recommendations become inaccurate as the user models age. These systems are therefore only employed in subjective domains where the user’s interests stay relatively unchanged and where the user perceives a direct benefit from rating items. A number of approaches to website data mining. Assigned to the i.

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Web Mining | Recommender Systems

http://recommender-systems.org/web-mining

Web mining is closely related to data mining, a process that discovers knowledge from large amounts of data without human interference. The term web mining is used when knowledge is discovered from internet data sources. Information filtering systems use web mining techniques for two types of web data. Content-based filtering systems abstract knowledge from web documents while collaborative filtering systems use information about web users. Matrix, where n. The fastest and simplest iterative method is th...