Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph

Autor:	Anindya Datta, Rajni Jindal, Kazunari Sugiyama, Upasna Bhandari
Rok vydání:	2013
Předmět:	World Wide Web GeneralLiterature_INTRODUCTORYANDSURVEY Computer science fungi mental disorders Similarity (psychology) Collaborative filtering Mobile apps food and beverages Graph (abstract data type) Recommender system Preference Domain (software engineering)
Zdroj:	Information Retrieval Technology ISBN: 9783642450679 AIRS
Popis:	Recommender systems can provide users with relevant items based on each user’s preferences. However, in the domain of mobile applications (apps), existing recommender systems merely recommend apps that users have experienced (rated, commented, or downloaded) since this type of information indicates each user’s preference for the apps. Unfortunately, this prunes the apps which are releavnt but are not featured in the recommendation lists since users have never experienced them. Motivated by this phenomenon, our work proposes a method for recommending serendipitous apps using graph-based techniques. Our approach can recommend apps even if users do not specify their preferences. In addition, our approach can discover apps that are highly diverse. Experimental results show that our approach can recommend highly novel apps and reduce over-personalization in a recommendation list.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4bece769f55c89bcd5e02cb897c22e2b https://doi.org/10.1007/978-3-642-45068-6_38 Zobrazit plný text záznamu