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- short-paperSeptember 2019
From preference into decision making: modeling user interactions in recommender systems
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 29–33https://doi.org/10.1145/3298689.3347065User-system interaction in recommender systems involves three aspects: temporal browsing (viewing recommendation lists and/or searching/filtering), action (performing actions on recommended items, e.g., clicking, consuming) and inaction (neglecting or ...
- abstractSeptember 2019
"Just play something awesome": the personalization powering voice interactions at Pandora
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPage 523https://doi.org/10.1145/3298689.3347064The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a delightful ...
- short-paperSeptember 2019
DualDiv: diversifying items and explanation styles in explainable hybrid recommendation
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 398–402https://doi.org/10.1145/3298689.3347063In recommender systems, item diversification and explainable recommendations improve users' satisfaction. Unlike traditional explainable recommendations that display a single explanation for each item, explainable hybrid recommendations display multiple ...
- extended-abstractSeptember 2019
Exploiting contextual information for recommender systems oriented to tourism
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 601–605https://doi.org/10.1145/3298689.3347062The use of contextual information like geographic, temporal (including sequential), and item features in Recommender Systems has favored their development in several different domains such as music, news, or tourism, together with new ways of evaluating ...
- demonstrationSeptember 2019
Darwin & Goliath: a white-label recommender-system as-a-service with automated algorithm-selection
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 534–535https://doi.org/10.1145/3298689.3347059Recommendations-as-a-Service (RaaS) ease the process for small and medium-sized enterprises (SMEs) to offer product recommendations to their customers. Current RaaS, however, suffer from a one-size-fits-all concept, i.e. they apply the same ...
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- extended-abstractSeptember 2019
ORSUM 2019 2nd workshop on online recommender systems and user modeling
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 562–563https://doi.org/10.1145/3298689.3347057The ever-growing nature of user generated data in online systems poses obvious challenges on how we process such data. Typically, this issue is regarded as a scalability problem and has been mainly addressed with distributed algorithms able to train on ...
- extended-abstractSeptember 2019
Workshop on recommender systems in fashion (fashionXrecsys2019)
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 552–553https://doi.org/10.1145/3298689.3347056Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. ...
- research-articleSeptember 2019
HybridSVD: when collaborative information is not enough
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 331–339https://doi.org/10.1145/3298689.3347055We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique. One of its key features is that it naturally extends a simple PureSVD approach and inherits its unique ...
- extended-abstractSeptember 2019
Recommender system for developing new preferences and goals
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 611–615https://doi.org/10.1145/3298689.3347054The research topic is to investigate how recommender systems can help people develop new preferences and goals. Recommender systems nowadays typically use historical user data to predict users' current preferences. However, users might want to develop ...
- short-paperSeptember 2019
Attribute-based evaluation for recommender systems: incorporating user and item attributes in evaluation metrics
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 378–382https://doi.org/10.1145/3298689.3347049Research in Recommender Systems evaluation remains critical to study the efficiency of developed algorithms. Even if different aspects have been addressed and some of its shortcomings - such as biases, robustness, or cold start - have been analyzed and ...
- demonstrationSeptember 2019
StoryTime: eliciting preferences from children for book recommendations
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 544–545https://doi.org/10.1145/3298689.3347048We present StoryTime, a book recommender for children. Our web-based recommender is co-designed with children and uses images to elicit their preferences. By building on existing solutions related to both visual interfaces and book recommendation ...
- research-articleSeptember 2019
FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 169–177https://doi.org/10.1145/3298689.3347043Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models ...
- short-paperSeptember 2019
Performance comparison of neural and non-neural approaches to session-based recommendation
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 462–466https://doi.org/10.1145/3298689.3347041The benefits of neural approaches are undisputed in many application areas. However, today's research practice in applied machine learning---where researchers often use a variety of baselines, datasets, and evaluation procedures---can make it difficult ...
- short-paperSeptember 2019
A simple multi-armed nearest-neighbor bandit for interactive recommendation
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 358–362https://doi.org/10.1145/3298689.3347040The cyclic nature of the recommendation task is being increasingly taken into account in recommender systems research. In this line, framing interactive recommendation as a genuine reinforcement learning problem, multi-armed bandit approaches have been ...
- research-articleSeptember 2019
CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 228–236https://doi.org/10.1145/3298689.3347038In Recommender Systems research, algorithms are often characterized as either Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained using a dataset of user preferences while CB algorithms are typically based on item profiles. ...
- short-paperSeptember 2019
Predicting online performance of job recommender systems with offline evaluation
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 477–480https://doi.org/10.1145/3298689.3347032At Indeed, recommender systems are used to recommend jobs. In this context, implicit and explicit feedback signals we can collect are rare events, making the task of evaluation more complex. Online evaluation (A/B testing) is usually the most reliable ...
- research-articleSeptember 2019
Adversarial attacks on an oblivious recommender
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 322–330https://doi.org/10.1145/3298689.3347031Can machine learning models be easily fooled? Despite the recent surge of interest in learned adversarial attacks in other domains, in the context of recommendation systems this question has mainly been answered using hand-engineered fake user profiles. ...
- short-paperSeptember 2019
Time slice imputation for personalized goal-based recommendation in higher education
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 506–510https://doi.org/10.1145/3298689.3347030Learners are often faced with the following scenario: given a goal for the future, and what they have learned in the past, what should they do now to best achieve their goal? We build on work utilizing deep learning to make inferences about how past ...
- short-paperSeptember 2019
Music recommendations in hyperbolic space: an application of empirical bayes and hierarchical poincaré embeddings
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 437–441https://doi.org/10.1145/3298689.3347029Matrix Factorization (MF) is a common method for generating recommendations, where the proximity of entities like users or items in the embedded space indicates their similarity to one another. Though almost all applications implicitly use a Euclidean ...
- research-articleSeptember 2019
Collective embedding for neural context-aware recommender systems
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 201–209https://doi.org/10.1145/3298689.3347028Context-aware recommender systems consider contextual features as additional information to predict user's preferences. For example, the recommendations could be based on time, location, or the company of other people. Among the contextual information, ...