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- abstractSeptember 2016
RecProfile '16: Workshop on Profiling User Preferences for Dynamic, Online, and Real-Time recommendations
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 411–412https://doi.org/10.1145/2959100.2959204This paper summarizes RecProfile '16, the first workshop on profiling user preferences for dynamic, online, and real-time recommendations, held in conjunction with RecSys '16, the 10th ACM conference on recommender systems. We describe the main themes ...
- tutorialSeptember 2016
People Recommendation Tutorial
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 431–432https://doi.org/10.1145/2959100.2959196People recommenders have become a rich research area within the broad recommender systems community and social recommender systems in particular. From "people you may know" and "who to follow" widgets, through people introduction at conferences, job ...
- short-paperSeptember 2016
MAPS: A Multi Aspect Personalized POI Recommender System
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 281–284https://doi.org/10.1145/2959100.2959187The evolution of the World Wide Web (WWW) and the smart-phone technologies have played a key role in the revolution of our daily life. The location-based social networks (LBSN) have emerged and facilitated the users to share the check-in information and ...
- research-articleSeptember 2016
Recommendations with a Purpose
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 7–10https://doi.org/10.1145/2959100.2959186The purpose of recommenders is often summarized as "help the users find relevant items", and the predominant operationalization of this goal has been to focus on the ability to numerically estimate the users' preferences for unseen items or to provide ...
- research-articleSeptember 2016Best Paper
Local Item-Item Models For Top-N Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 67–74https://doi.org/10.1145/2959100.2959185Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in ...
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- short-paperSeptember 2016
A Package Recommendation Framework for Trip Planning Activities
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 203–206https://doi.org/10.1145/2959100.2959183Classical recommender systems provide users with ranked lists of recommendations, where each one consists of a single item. However, these ranked lists are not suitable for applications such as trip planning, which deal with heterogeneous items. In this ...
- research-articleSeptember 2016
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 59–66https://doi.org/10.1145/2959100.2959182Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, ...
- research-articleSeptember 2016
Ask the GRU: Multi-task Learning for Deep Text Recommendations
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 107–114https://doi.org/10.1145/2959100.2959180In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor ...
- short-paperSeptember 2016
Contrasting Offline and Online Results when Evaluating Recommendation Algorithms
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 31–34https://doi.org/10.1145/2959100.2959176Most evaluations of novel algorithmic contributions assess their accuracy in predicting what was withheld in an offline evaluation scenario. However, several doubts have been raised that standard offline evaluation practices are not appropriate to ...
- short-paperSeptember 2016
Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 119–122https://doi.org/10.1145/2959100.2959175Computing useful recommendations for cold-start users is a major challenge in the design of recommender systems, and additional data is often required to compensate the scarcity of user feedback. In this paper we address such problem in a target domain ...
- research-articleSeptember 2016
Latent Factor Representations for Cold-Start Video Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 99–106https://doi.org/10.1145/2959100.2959172Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. To alleviate this problem, item content representation (mostly in textual form) has been used as auxiliary ...
- research-articleSeptember 2016
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 91–98https://doi.org/10.1145/2959100.2959170Conventional collaborative filtering techniques treat a top-n recommendations problem as a task of generating a list of the most relevant items. This formulation, however, disregards an opposite -- avoiding recommendations with completely irrelevant ...
- research-articleSeptember 2016
Convolutional Matrix Factorization for Document Context-Aware Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 233–240https://doi.org/10.1145/2959100.2959165Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information ...
- research-articleSeptember 2016
Algorithms Aside: Recommendation As The Lens Of Life
- Tamas Motajcsek,
- Jean-Yves Le Moine,
- Martha Larson,
- Daniel Kohlsdorf,
- Andreas Lommatzsch,
- Domonkos Tikk,
- Omar Alonso,
- Paolo Cremonesi,
- Andrew Demetriou,
- Kristaps Dobrajs,
- Franca Garzotto,
- Ayşe Göker,
- Frank Hopfgartner,
- Davide Malagoli,
- Thuy Ngoc Nguyen,
- Jasminko Novak,
- Francesco Ricci,
- Mario Scriminaci,
- Marko Tkalcic,
- Anna Zacchi
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 215–219https://doi.org/10.1145/2959100.2959164In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen ...
- short-paperSeptember 2016
Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 75–78https://doi.org/10.1145/2959100.2959161We introduce an asynchronous distributed stochastic gradient algorithm for matrix factorization based collaborative filtering. The main idea of this approach is to distribute the user-rating matrix across different machines, each having access only to a ...
- research-articleSeptember 2016
Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 51–58https://doi.org/10.1145/2959100.2959159Existing feature-based recommendation methods incorporate auxiliary features about users and/or items to address data sparsity and cold start issues. They mainly consider features that are organized in a flat structure, where features are independent ...
- research-articleSeptember 2016
HCI for Recommender Systems: the Past, the Present and the Future
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 123–126https://doi.org/10.1145/2959100.2959158How can you discover something new, that matches your interest? Recommender Systems have been studied since the 90ies. Their benefit comes from guiding a user through the density of the information jungle to useful knowledge clearings. Early research on ...
- research-articleSeptember 2016
Representation Learning for Homophilic Preferences
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 317–324https://doi.org/10.1145/2959100.2959157Users express their personal preferences through ratings, adoptions, and other consumption behaviors. We seek to learn latent representations for user preferences from such behavioral data. One representation learning model that has been shown to be ...
- research-articleSeptember 2016
Crowd-Based Personalized Natural Language Explanations for Recommendations
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 175–182https://doi.org/10.1145/2959100.2959153Explanations are important for users to make decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people ...
- research-articleSeptember 2016
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 309–316https://doi.org/10.1145/2959100.2959152Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard' recommender systems challenges, such as ...