Item2Vec: Neural Item Embedding for Collaborative Filtering

Code

• github.com/nnkkmto/item2vec-food-rec
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• github.com/hyunbool/item_recommender_with_nlp
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• github.com/hyunbool/frequent_itemset_mining_using_language_model
  tf★ 0
• github.com/nnkkmto/item2vec-rec-test
  none★ 0
• github.com/hyunbool/Frequent-Pattern-Mining-using-Language-Model
  tf★ 0
• github.com/hyunbool/item2vec_movie_practice
  tf★ 0

Abstract

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.

Tasks

Reproductions