Beyond accuracy: evaluating recommender systems by coverage and serendipity
Proceedings of the fourth ACM conference on Recommender systems
Personal-Hosting RESTful web services for social network based recommendation
ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing
A folksonomy-based recommender system for personalized access to digital artworks
Journal on Computing and Cultural Heritage (JOCCH)
Penguins in sweaters, or serendipitous entity search on user-generated content
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Workshop and challenge on news recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Modeling and broadening temporal user interest in personalized news recommendation
Expert Systems with Applications: An International Journal
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Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/ searched/ visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen.This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions.