From Word Embeddings to Item Recommendation

2016-01-07 · Makbule Gulcin Ozsoy

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.

📄 PDF Abstract BibTeX arXiv:1601.01356

Code (2)

caroline171/content_based_recommendation

mgulcin/DL_Rec

Tasks

Recommendation SystemsWord Embeddings

Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation

2023-11-17 · Shenghao Yang, Chenyang Wang, Yankai Liu, Kangping Xu 외

Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually utilize ID embeddings to represent items, …

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2020-10-24 · Xavier Thomas

Recommender systems are a class of machine learning algorithms that provide relevant recommendations to a user based on the user's interaction with similar items or based on the content of the item. In settings where the…

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2021-05-18 · Tong Zhang, Yong liu, Peixiang Zhong, Chen Zhang 외

The chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions. To better understand user's intentions, external knowledge graphs (KG) have bee…

Conversational Recommendation Entity Embeddings Knowledge Graphs Recommendation Systems+2

RecJPQ: Training Large-Catalogue Sequential Recommenders

2023-12-11 · Aleksandr V. Petrov, Craig Macdonald

Sequential Recommendation is a popular recommendation task that uses the order of user-item interaction to model evolving users' interests and sequential patterns in their behaviour. Current state-of-the-art Transformer-…

Passage Retrieval Recommendation Systems Retrieval Sequential Recommendation

Metadata Embeddings for User and Item Cold-start Recommendations

2015-07-30 · Maciej Kula

I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features' latent factors. The model outperforms both collaborative and content-based models in cold-start…

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