15 research outputs found
A switching multi-level method for the long tail recommendation problem
Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users’ rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail
Report From the Hymenoptera Committee of the Spanish Society of Allergology and Clinical Immunology: Immunotherapy With Bumblebee Venom
Les divergences curriculum - évaluation certificative dans les écoles primaires bilingues de pays du sud: Conséquences du point de vue de l'acquisition du bilinguisme
The accuracy of the diagnosis of suspected macrolide antibiotic hypersensitivity: results of a single-blinded trial
I. Ediciones y tĂ©cnica filolĂłgica – II. LingĂĽĂstica – III. Literatura, filosofĂa y religiĂłn – IV. Historia y sociedad
New protocol based on massive parallel sequencing for aneuploidy screening of preimplantation human embryos
I. Ediciones y tĂ©cnica filolĂłgica – II. LingĂĽĂstica — III. Literatura, historia y filosofĂa – IV. Reseñas Breves
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