Exploration of Word Embedding Model to Improve Context-Aware Recommender Systems
作者： Camila Sundermann , João Antunes , Marcos Domingues , Solange Rezende , None
关键词:
摘要: Recommender systems aim to assist users by recommending items that may be of interest them. Traditionally, these use only user and item information. Over time, new information is being used, such as contextual information, which has improved the accuracy generated recommendations. In this work, we propose a context-aware recommender method extracts from textual reviews using word embedding based model. addition, two ways considering contexts in systems, "Context Reviews" Items". We evaluated our proposal Yelp dataset (RecSysChallenge 2013); three baselines; four systems. general, seems superior baselines, mainly Items", results were promising, allowing some lines future work.
sci-hub.se 参考文章(23)
Aansi A. Kothari, Warish D. Patel, A Novel Approach Towards Context Sensitive Recommendations Based on Machine Learning Methodology international conference on communication systems and network technologies. pp. 1114- 1118 ,(2015) , 10.1109/CSNT.2015.191
Anind K. Dey, Understanding and Using Context ubiquitous computing. ,vol. 5, pp. 4- 7 ,(2001) , 10.1007/S007790170019
Francesco Colace, Massimo De Santo, Luca Greco, Vincenzo Moscato, Antonio Picariello, A collaborative user-centered framework for recommending items in Online Social Networks Computers in Human Behavior. ,vol. 51, pp. 694- 704 ,(2015) , 10.1016/J.CHB.2014.12.011
Asher Levi, Osnat Mokryn, Christophe Diot, Nina Taft, Finding a needle in a haystack of reviews Proceedings of the sixth ACM conference on Recommender systems - RecSys '12. pp. 305- 306 ,(2012) , 10.1145/2365952.2365977
K. Veningston, R. Shanmugalakshmi, Personalized location aware recommendation system international conference on advanced computing. pp. 1- 6 ,(2015) , 10.1109/ICACCS.2015.7324140
Takumi Takehara, Shohei Miki, Naoko Nitta, Noboru Babaguchi, Extracting Context Information from Microblog Based on Analysis of Online Reviews international conference on multimedia and expo. pp. 248- 253 ,(2012) , 10.1109/ICMEW.2012.49
Umberto Panniello, Michele Gorgoglione, Incorporating context into recommender systems: an empirical comparison of context-based approaches Electronic Commerce Research. ,vol. 12, pp. 1- 30 ,(2012) , 10.1007/S10660-012-9087-7
Marcos Aurelio Domingues, Camila Vaccari Sundermann, Marcelo Garcia Manzato, Ricardo Marcondes Marcacini, Solange Oliveira Rezende, Exploiting Text Mining Techniques for Contextual Recommendations 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). ,vol. 2, pp. 210- 217 ,(2014) , 10.1109/WI-IAT.2014.100
Marcos Aurélio Domingues, Alípio Mário Jorge, Carlos Soares, Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems Information Processing and Management. ,vol. 49, pp. 698- 720 ,(2013) , 10.1016/J.IPM.2012.07.009
Carl Kadie, David Heckerman, John S. Breese, Empirical analysis of predictive algorithms for collaborative filtering uncertainty in artificial intelligence. pp. 43- 52 ,(1998)