TY - GEN
T1 - ITEM2VEC
T2 - 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
AU - Barkan, Oren
AU - Koenigstein, Noam
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/8
Y1 - 2016/11/8
N2 - Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as word2vec, was shown to provide state-of-the-art results on various linguistics tasks. In this paper, we show that item-based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe a method we name item2vec for item-based CF that produces embedding for items in a latent space. The method is capable of inferring item-item relations even when user information is not available. We present experimental results that demonstrate the effectiveness of the item2vec method and show it is competitive with SVD.
AB - Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as word2vec, was shown to provide state-of-the-art results on various linguistics tasks. In this paper, we show that item-based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe a method we name item2vec for item-based CF that produces embedding for items in a latent space. The method is capable of inferring item-item relations even when user information is not available. We present experimental results that demonstrate the effectiveness of the item2vec method and show it is competitive with SVD.
KW - collaborative filtering
KW - item recommendations
KW - item similarity
KW - item-item collaborative filtering
KW - market basket analysis
KW - neural word embedding
KW - recommender systems
KW - skip-gram
KW - word2vec
UR - http://www.scopus.com/inward/record.url?scp=85002388961&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2016.7738886
DO - 10.1109/MLSP.2016.7738886
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85002388961
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
A2 - Diamantaras, Kostas
A2 - Uncini, Aurelio
A2 - Palmieri, Francesco A. N.
A2 - Larsen, Jan
PB - IEEE Computer Society
Y2 - 13 September 2016 through 16 September 2016
ER -