Titlebook: Recommender Systems Handbook; Francesco Ricci,Lior Rokach,Bracha Shapira Book 2022Latest edition Springer Science+Business Media, LLC, par

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Semantics and Content-Based Recommendationson . features, which are obtained by processing . or . characteristics of the items. In this setting, the adoption of semantics-aware representations can be very useful to build a more precise representation of users and items, and, in turn, to generate better recommendations. To this end, this chap

箴言

玩忽职守

Adversarial Recommender Systems: Attack, Defense, and Advancesst them. Recently, recommender systems have been shown vulnerable to adversarial attacks that force the models to produce misleading recommendations. For instance, adversaries can attempt to push target items into high/low positions in the recommendation lists by inserting optimized fake profiles in

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People-to-People Reciprocal Recommenderss they must satisfy the preferences and needs of the two parties involved in the recommendation. In contrast, the traditional items-to-people recommenders are one sided and must satisfy only the preference of the person for whom the recommendation is generated. We review the characteristics of recip

观点

Natural Language Processing for Recommender Systemsformation on users’ preferences or items’ traits. Arguably, the most meaningful signal for recommenders is textual data, which includes examples like user-generated reviews, textual-item descriptions and even conversational interaction in natural language. Additionally, the output of a typical recom

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Design and Evaluation of Cross-Domain Recommender Systemsms, reflecting a spectrum of their tastes and interests. Leveraging all the user preferences available in several systems or domains may be beneficial for generating more encompassing user models and better recommendations, e.g., through mitigating the cold-start and sparsity problems, or enabling c

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Evaluating Recommender Systemsses a system designer that wishes to employ a recommendater system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommender systems have a var