Collaborative and Social Information Retrieval and Access: by Max Chevalier, Christine Julien, Visit Amazon's Chantal

By Max Chevalier, Christine Julien, Visit Amazon's Chantal Soule-Dupuy Page, search results, Learn about Author Central, Chantal Soule-Dupuy,

Execs are continuously provided with various info assets developing the necessity to make certain their relevance in the large quantity of obtainable info.

Collaborative and Social details Retrieval and entry: thoughts for stronger consumer Modeling offers present cutting-edge advancements together with case experiences, demanding situations, and developments. protecting themes reminiscent of recommender structures, consumer profiles, and collaborative filtering, this ebook informs and educates academicians, researchers, and box practitioners at the most recent developments in details retrieval.

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A framework for collaborative, content-based and demographic filtering. In Artificial Intelligence Review, 13(5-6), 393–408. , & Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. In 18th National Conference on Artificial Intelligence (pp. 187-192). , & Giles, C. L. (2000). Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. In 16th Conference on Uncertainty in Artificial Intelligence (pp. 473–480). , & Riedl, J. (2003).

Each algorithm produced five recommendations. e. if a user noted that they liked Titanic then it would not be recommended to them). The fifteen total recommendations were randomly ordered in a list with duplicate recommendations being removed. Such duplicates could be produced by different the different algorithms recommending the same film. The participant was then asked to score each of the recommendations. If they had already seen the film then they were asked to give a score as to how much they liked it, on a scale from 1-5 (1 being the least and 5 being the most).

The two categories of solutions also differ in their interpretation of the users operating within the system. Memorybased methods model all user interactions based on measurable similarity-values, and thus leads to the notion of a community of recommenders. Model-based approaches, instead, train a separate model for each user in the system, and are thus characterized by a stronger subjective view of the recommender system’s end users. Hybrid Methods As we have seen, filtering algorithms have been designed from a number of different backgrounds, leading to the categorization of these algorithms into memory- and model-based groups.

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