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RecSys 2016: Boston, MA, USA - Posters
- Ido Guy, Amit Sharma:
Proceedings of the Poster Track of the 10th ACM Conference on Recommender Systems (RecSys 2016), Boston, USA, September 17, 2016. CEUR Workshop Proceedings 1688, CEUR-WS.org 2016 - Panagiotis Symeonidis, Dimitrios Malakoudis:
MoocRec.com : Massive Open Online Courses Recommender System. - Elja Arjas, Arnoldo Frigessi, Valeria Vitelli, Marta Crispino:
Recommendation from Intransitive Pairwise Comparisons. - Remo Manuel Frey, Denis Vuckovac, Alexander Ilic:
A Secure Shopping Experience Based on Blockchain and Beacon Technology. - Masahiro Kazama, István Varga:
Cross Domain Recommendation Using Vector Space Transfer Learning. - Nevena Dragovic, Maria Soledad Pera:
Genre Prediction to Inform the Recommendation Process. - Sneha Chaudhari, Amos Azaria, Tom M. Mitchell:
An Entity Graph Based Recommender System. - Mattia Brusamento, Roberto Pagano, Martha A. Larson, Paolo Cremonesi:
Explicit Elimination of Similarity Blocking for Session-based Recommendation. - Oren Barkan, Yael Brumer, Noam Koenigstein:
Modelling Session Activity with Neural Embedding. - Stephanie Yang, Max Sklar:
Detecting Trending Venues Using Foursquare's Data. - Rainer Schlosser, Martin Boissier, Andre Schober, Matthias Uflacker:
How to Survive Dynamic Pricing Competition in E-commerce. - Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Weighted Random Walks for Meta-Path Expansion in Heterogeneous Networks. - Ludovik Çoba, Markus Zanker:
rrecsys: An R-package for Prototyping Recommendation Algorithms. - Oren Barkan, Noam Koenigstein:
Item2vec: Neural Item Embedding for Collaborative Filtering. - Mario Scriminaci, Andreas Lommatzsch, Benjamin Kille, Frank Hopfgartner, Martha A. Larson, Davide Malagoli, András Serény, Till Plumbaum:
Idomaar: A Framework for Multi-dimensional Benchmarking of Recommender Algorithms. - Enrique Cruz, Berk Kapicioglu:
Tip Ranker: A M.L. Approach to Ranking Short Reviews. - Moshe Unger, Bracha Shapira, Lior Rokach, Ariel Bar:
Deep Auto-Encoding for Context-Aware Inference of Preferred Items' Categories. - Chih-Ming Chen, Chun-Yao Yang, Chih-Chun Hsia, Yian Chen, Ming-Feng Tsai:
Music Playlist Recommendation via Preference Embedding. - Chih-Yu Chao, Yi-Fan Chu, Yi Ho, Chuan-Ju Wang, Ming-Feng Tsai:
Dish Discovery via Word Embeddings on Restaurant Reviews. - Joseph Jay Williams, Luong Hoang:
Combining Dynamic A/B Experimentation and Recommender Systems in MOOCs. - Tim Donkers, Benedikt Loepp, Jürgen Ziegler:
Towards Understanding Latent Factors and User Profiles by Enhancing Matrix Factorization with Tags. - Ion Madrazo Azpiazu, Maria Soledad Pera:
Is Readability a Valuable Signal for Hashtag Recommendations? - Evangelia Anagnostopoulou, Efthimios Bothos, Babis Magoutas, Gregoris Mentzas:
Memory Priming and User Preferences. - Ludovico Boratto, Salvatore Carta, Gianni Fenu, Roberto Saia:
Representing Items as Word-Embedding Vectors and Generating Recommendations by Measuring their Linear Independence. - Farzad Eskandanian, Bamshad Mobasher, Robin D. Burke:
User Segmentation for Controlling Recommendation Diversity.
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