From Word Embeddings to Item Recommendation

Abstract

Social network platforms can use the data produced by their users to serve them better. One of the services these platforms provide is recommendation service. Recommendation systems can predict the future preferences of users using their past preferences. In the recommendation systems literature there are various techniques, such as neighborhood based methods, machine-learning based methods and matrix-factorization based methods. In this work, a set of well known methods from natural language processing domain, namely Word2Vec, is applied to recommendation systems domain. Unlike previous works that use Word2Vec for recommendation, this work uses non-textual features, the check-ins, and it recommends venues to visit/check-in to the target users. For the experiments, a Foursquare check-in dataset is used. The results show that use of continuous vector space representations of items modeled by techniques of Word2Vec is promising for making recommendations.

References in corpus (2)

• Distributed Representations of Words and Phrases and their Compositionality
• Distributed Representations of Sentences and Documents

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• Semantic Representation and Inference for NLP
• A Hybrid Approach to Enhance Pure Collaborative Filtering based on Content Feature Relationship
• Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences
• BoostJet: Towards Combining Statistical Aggregates with Neural Embeddings for Recommendations
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• Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling
• Utilizing FastText for Venue Recommendation