ABSTRACT
Neural networks and word embeddings are powerful tools to capture latent factors. These tools can provide effective measures of similarities between users or items in the context of sparse data. We propose a novel approach that relies on neural networks and word embeddings to the problem of matching a learner looking for mentoring, and a tutor that is willing to provide this mentoring. Tutors and learners can issue multiple offers/requests on different topics. The approach matches over the whole array of topics specified by learners and tutors. Its performance for tutor-learner matching is compared with the state of the art. It yields similar results in terms of precision, but improves the recall.
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Index Terms
Encoding User as More Than the Sum of Their Parts: Recurrent Neural Networks and Word Embedding for People-to-people Recommendation
Recommendations
Hybrid group recommendations for a travel service
Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such ...
Social group recommendation in the tourism domain
Recommender Systems learn users' preferences and tastes in different domains to suggest potentially interesting items to users. Group Recommender Systems generate recommendations that intend to satisfy a group of users as a whole, instead of individual ...
Accounting for taste: using profile similarity to improve recommender systems
CHI '06: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsRecommender systems have been developed to address the abundance of choice we face in taste domains (films, music, restaurants) when shopping or going out. However, consumers currently struggle to evaluate the appropriateness of recommendations offered. ...
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