Item2vec: Neural item embedding for collaborative filtering

Oren Barkan, Noam Koenigstein

פרסום מחקרי: פרסום בכתב עתמאמר מכנסביקורת עמיתים

21 ציטוטים ‏(Scopus)

תקציר

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.

שפה מקוריתאנגלית
כתב עתCEUR Workshop Proceedings
כרך1688
סטטוס פרסוםפורסם - 2016
פורסם באופן חיצוניכן
אירוע10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, ארצות הברית
משך הזמן: 15 ספט׳ 201619 ספט׳ 2016

טביעת אצבע

להלן מוצגים תחומי המחקר של הפרסום 'Item2vec: Neural item embedding for collaborative filtering'. יחד הם יוצרים טביעת אצבע ייחודית.

פורמט ציטוט ביבליוגרפי