We look forward to welcoming you to the 13th RecSysNL meetup. Hosted by Bookarang, the meetup is held at the Volkshotel, Wibautstraat 150, Amsterdam with two interesting talks, one from industry and one from academia.
Doors open at 18:00h, talks start at 18:30h.
Drinks and snacks/pizza will be provided.
- 1st talk by Niels Bogaards and Isaac Sijaranamual from Bookarang, "Content based recommendations for books"
Choosing a book is a difficult task: books take a significant amount of time to read, come in vast numbers and differ on very subtle qualities. Book taste is highly personal: for the same book there can be as many readers that love as that hate it. By modelling the content of books and comparing them on aspects that matter to readers, Bookarang can provide recommendations that are explainable, personalised and tuned to a shop, library or user's preferences.
Niels Bogaards is an expert in the field of Artificial Intelligence and worked at the world-famous IRCAM in Paris.
Isaac Sijaranamual studied Informatics and worked as a scientific programmer. He is an expert in the field of Machine Learning, Natural Language Processing and Information Retrieval.
- 2nd talk by Marijn Koolen from Royal Netherlands Academy of Arts and Sciences, Netherlands, "Narrative-driven Recommendation for Casual-Leisure Needs"
Recommender systems typically generate recommendations for a user based on their profile, or for an item given its user interactions, but there are many scenarios especially in leisure domains such as books, movies, games and music, where users have specific recommendation needs, where they want to steer the recommendation process towards certain aspects they find relevant. Currently, there are few recommender or search systems that can deal with the complexity of such directed needs, nor do we know well which data types (metadata, user ratings and reviews, item content) are useful to match against different aspects of recommendation needs. There are many discussion forums where users describe their needs and their frustration with current search and recommender systems. In this talk I will summarize our work on analyzing relevance aspects for these needs and describe experiments on dealing with these.
Marijn Koolen is researcher and developer at the Royal Academy of Arts and Sciences, and works on bestseller prediction, reading impact analysis and transparent recommender systems. He has a background in information retrieval and web search and got his PhD at the University of Amsterdam for work on hyperlink structure in information retrieval.