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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 ...
- invited-talkSeptember 2016
Personalization for Google Now: User Understanding and Application to Information Recommendation and Exploration
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Page 3https://doi.org/10.1145/2959100.2959192At the heart of any personalization application, such as Google Now, is a deep model for users. The understanding of users ranges from raw history to lower dimensional reductions like interest, locations, preferences, etc. We will discuss different ...
- invited-talkSeptember 2016
Automated Machine Learning in the Wild
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Page 1https://doi.org/10.1145/2959100.2959191Machine Learning research is progressing at an ever increasing pace. Fueled by technology advances commonly referred to as "Big Data", all data related fields are teaming with scientific and applied activity: our communities explore new application ...
- 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 ...
- 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 ...
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- 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 ...
- research-articleSeptember 2016
Using Navigation to Improve Recommendations in Real-Time
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 341–348https://doi.org/10.1145/2959100.2959174Implicit feedback is a key source of information for many recommendation and personalization approaches. However, using it typically requires multiple episodes of interaction and roundtrips to a recommendation engine. This adds latency and neglects the ...
- 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 ...
- short-paperSeptember 2016Best Short Paper
Adaptive, Personalized Diversity for Visual Discovery
- Choon Hui Teo,
- Houssam Nassif,
- Daniel Hill,
- Sriram Srinivasan,
- Mitchell Goodman,
- Vijai Mohan,
- S.V.N. Vishwanathan
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 35–38https://doi.org/10.1145/2959100.2959171Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired. Visual browsing systems allow e-commerce platforms to address these scenarios ...
- 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 ...
- 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
Joint User Modeling across Aligned Heterogeneous Sites
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 83–90https://doi.org/10.1145/2959100.2959155An accurate and comprehensive user modeling technique is crucial for the quality of recommender systems. Traditionally, we model user preferences using only actions from the target site and may suffer from cold-start problem. As nowadays people normally ...
- 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 ...
- research-articleSeptember 2016
TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 261–268https://doi.org/10.1145/2959100.2959151We address the challenge of personalized recommendation of high quality content producers in social media. While some candidates are easily identifiable (say, by being "favorited" many times), there is a long-tail of potential candidates for whom we ...
- research-articleSeptember 2016
Discovering What You're Known For: A Contextual Poisson Factorization Approach
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 253–260https://doi.org/10.1145/2959100.2959146Discovering what people are known for is valuable to many important applications such as recommender systems. Unlike an individual's personal interests, what a user is known for is reflected by the views of others, and is often not easily discerned for ...
- short-paperSeptember 2016
Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 269–272https://doi.org/10.1145/2959100.2959141Conformity has a strong influence to user behaviors, even in online environment. When surfing online, users are usually flooded with others' opinions. These opinions implicitly contribute to the user's ongoing behaviors. However, there is no research ...
- short-paperSeptember 2016
Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 139–142https://doi.org/10.1145/2959100.2959140Prior work relevant to incorporating personality into recommender systems falls into two categories: social science studies and algorithmic ones. Social science studies of preference have found only small relationships between personality and category ...
- short-paperSeptember 2016
A Scalable Approach for Periodical Personalized Recommendations
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 23–26https://doi.org/10.1145/2959100.2959139We develop a highly scalable and effective contextual bandit approach towards periodical personalized recommendations. The online bootstrapping-based technique provides a principled way for UCB-type exploitation-exploration algorithms, while being able ...
- research-articleSeptember 2016
Recommending New Items to Ephemeral Groups Using Contextual User Influence
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsSeptember 2016, Pages 285–292https://doi.org/10.1145/2959100.2959137Group recommender systems help groups of users in finding appropriate items to be enjoyed together. Lots of activities, like watching TV or going to the restaurant, are intrinsically group-based, thus making the group recommendation problem very ...