Context-aware recommender systems and cultural heritage: a survey

Abstract

In the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.

Competitive gamification in crowdsourcing-based contextual-aware recommender systems

“…Context-aware recommender systems (CARSs) that incorporate contextual information into the recommendation process are widely applied in domains such as music and video contexts ( Alhamid et al., 2013 ), cultural heritage ( Casillo et al., 2023 ; Pavlidis, 2019 ), places of interest ( Clarizia et al., 2019 ; Kabassi, 2010 ), and shopping information ( Shi et al., 2015 ). However, previous studies rarely examine CARSs that consider contextual information in contactless manners.…”

Considering emotions and contextual factors in music recommendation: a systematic literature review

“…In the field of recommendation systems, context can be the user's situation when looking for recommendations (e.g., time, mood, current activity). Clearly, such information can influence the user's need for information, therefore, can enrich conventional knowledge such as user's preferences when providing recommendations [19]. In line with Magara's observations [85], users create music playlists manually for various contexts and activities in which they are interested.…”

Recommender systems: a novel approach based on singular value decomposition

“…Among the main types of RS are content-based systems, collaborative systems, and hybrid systems [6]. These are also advanced recommendation services that manage and use the entire context in which a user is located: RS based on context-aware technologies [7], [8]. As previously mentioned, one of the main characteristics of recommender systems is to predict the consideration that an individual may have about an item that has not yet been evaluated.…”