Abstract
With the explosive growth of the data that being produced and published on the Web every day by the scientific community, it is becoming difficult for researchers to find the most appropriate scientific articles for their needs. For alleviate such information overload, the recommender systems plays a key role in allowing users to access what interests them as quickly as possible.
This is why we are going to focus on finding the best approach that can be supported in scientific articles recommendation systems, to be able to guide the researchers in finding articles in an effective way.
This paper presents a comparison between the main Recommender Systems techniques that aims to recommend to users the relevant articles, according to preferences and habits. Preference and relevance are subjective and are generally derived from items previously consumed by users. We chose here three most used techniques; first, collaborative filtering, then content-based systems and finally a Hybrid recommendation. To evaluate the recommendation we have used classical measures in search of information: precision at top k, recall at top k, NDCG@k and novelty of the recommended items.
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El Alaoui, D., Riffi, J., Aghoutane, B., Sabri, A., Yahyaouy, A., Tairi, H. (2021). Overview of the Main Recommendation Approaches for the Scientific Articles. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2021. Lecture Notes in Business Information Processing, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-030-76508-8_9
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