Traditional approaches for music recommender systems face the known challenges of providing new recommendations that users perceive as novel and serendipitous discoveries. Even wi...
Complex heterogeneous networks contain many types of relations, both local to a particular entity and distant in the network. Multi-relational factorization schemes that incorpora...
Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher
We describe an architecture for generating context-aware recommendations along with detailed textual explanations to support the user in the decision-making process. CARE (Context...
This paper describes some of the key properties of the proposed solution for the RecSys 2015 Challenge from the team Tøyvind thørrud. Three contributions will be highlighted: i)...
Over the last years, thanks to Open Data initiative and the Semantic Web, there has been a vast increase on user contributed data. In several cases (e.g. OpenStreetMap, Geonames),...
This work shows the early stages of the development of a collaborative-filtering-inspired adaptive system to streamline the ordering process at restaurants that use electronic me...
The 3rd International Workshop on News Recommendation and Analytics (INRA 2015) is held in conjunction with RecSys 2015 Conference in Vienna, Austria. This paper presents a brief ...
We propose an alternative way to efficiently exploit rating data for collaborative filtering with Factorization Machines (FMs). Our approach partitions user-item matrix into ‘...
An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on lon...
With the increasing accessibility of transactional data in venture finance, venture capital firms (VCs) face great challenges in developing quantitative tools to identify new in...