FRINK Linked Data Fragments server

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• W3157941259 abstract "The major aim of a recommender systems (RSs) is to offer references on numerous products to the consumers. An RS is well-defined as a self-operating system that, given a consumer model and a set of available items such as groceries, books, news, and movies, can offer items that are more fascinating to the consumer. This model generates recommendations by adopting either explicit or implicit data about the user inclinations. Mostly RSs are built on the amount of likeness between either the target user or the target item with all other users or items. By gathering data about the users' favorites for dissimilar items, an RS produces their summaries. These favorites can assist the RS to predict remaining items that might be exciting to the user in the future. Collaborative and content-based filtering is the most frequently used technique to generate recommendations. Collaborative filtering generates recommendations based on past ratings, while content filtering generates recommendations based on ratings and features. Hence, the users' profiles must be accessible to the RS to obtain recommendations. The desirable features of an RS are (i) to be nimble, (ii) disseminated, and (iii) secure. A high-speed RS does not divert the attention of the customer in shopping. A disseminated RS avoids creating centralization, which in turn improves the availability of the items. Lastly, a secure RS protects sensitive information from intruders. From the perceptive of security, there are two major concerns looked for in an RS: (i) protection of the users' personal information, ratings, and storage and (ii) generation of recommendations. Recommenders provide personalized recommendations by taking into consideration not only the users' summaries but also personal information like past item purchasing history, demographics, and online behavior sent to the recommender. Therefore, the users' profiles comprise individual and highly subtle information, such as their summarized browsing history, opinions, likes, and dislikes. It is thus vital to sufficiently secure individual information managed by an RS. The second task that the RS involves is to secure all the possible access paths among the system and the user such as the users' rating data for a store, from the store to the database, from a database to RS, and finally from RS to the users' recommendations. If a security threat occurs between the user and the store access path the intruder might modify the users' ratings; if it occurs in between the store and database the intruder might modify the similarity and aggregate computation; and if it occurs in between the database and recommender server or in between the recommender server and the user the intruder might falsify the entire recommendation process. Approaches to secure the rating matrix, similarity and aggregate computations, encrypting top items, building trusted networks, and detecting attachment paths are the possible ways to divert intruder's attention without conceding the accurate rating information. We address the occurrence of these privacy breaks in the RS, which is possible to study in the approaching direction of the chapter." @default.
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• W3157941259 date "2021-01-01" @default.
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• W3157941259 title "Recommender systems" @default.
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• W3157941259 doi "https://doi.org/10.1016/b978-0-12-821599-9.00007-8" @default.
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