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Other ways have been explored in the field to enhance the power of these systems by integrating the context as an additional attribute. This inclusion tries to extract the user preferences more accurately taking into account multiple components such as temporal, spatial, or social ones. Notwithstanding the magnitude of context-awareness in this area, the research community is in agreement with the lack of framework for context information and how to integrate it into recommender systems. Under this premise, this paper focuses on a comprehensive systematic literature review of the state-of-the-art recommendation techniques and their characteristics to benefit from contextual information. The following survey presents the following contributions as outcomes of our study: (i) determine a framework where multiple aspects are taken into account to have a clear definition of context representation, (ii) the techniques used to incorporate context, and (iii) the evaluation of these methods in terms of reproducibility and effectiveness. Our review also covers some crucial topics about context integration, classification of the contexts, application domains, and evaluation of the used datasets, metrics, and code implementations, where we observed clear shiftings in algorithmic and evaluation trends towards Neural Network approaches and ranking metrics, respectively. Just as importantly, future research opportunities and directions are exposed as final closure, standing out the exploitation of various data sources and the scalability and customization of existing solutions.<\/jats:p>","DOI":"10.1007\/s10462-024-10939-4","type":"journal-article","created":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T03:01:21Z","timestamp":1731726081000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["A systematic literature review of recent advances on context-aware recommender systems"],"prefix":"10.1007","volume":"58","author":[{"given":"Pablo","family":"Mateos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alejandro","family":"Bellog\u00edn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"issue":"3","key":"10939_CR1","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1007\/s11063-021-10475-0","volume":"53","author":"S Abinaya","year":"2021","unstructured":"Abinaya S, Devi MKK (2021) Enhancing top-N recommendation using stacked autoencoder in context-aware recommender system. 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