## 3 Semantic Educational Recommender Systems
### Related Works  
Recommender systems have shown their success in many domains where information overload exists [6]. This has motivated their application in e-learning settings. Two perspectives can be considered when developing recommender systems in the educational domain: (i) a top-down approach suitable for formal e-learning, where the structure, learning materials and learning plans are maintained by domain professionals; and (ii) a bottom-up approach suitable for non-formal e-learning as it takes place in a self directed way within learning networks, where learners interact with information sources shared in the network [24]. Moreover, formal and non-formal e-learning scenarios offer a rather different context that has to be taken into account by recommender systems in order to offer personalized information to individual learners [25]. To apply recommender systems for the educational domain, and specially, for formal e-learning scenarios, an important issue is to determine whether both domains, _i.e._, e-commerce and education, require a different design of the recommender. [PERSON] _et al._ state that both domains share the need to manage design activities before the runtime and also during the maintenance of the system [9]. However, recommender systems in education are quite different from recommender systems in e-commerce, as they have to consider not only the learners or the educator's preferences for a certain material, but also how this material may help them to achieve their goals [26]. A critical issue that both domains share is the need of building rich datasets to facilitate their development and benchmarking. Dataset-driven research can be followed to build recommender systems for educational scenarios. It has not been until very recently that datasets with information related to educational content have started to be compiled. In particular, it has been explored in the literature datasets that capture learner interactions with tools and resources [27], although they cannot be considered fully educational data sets, as the datasets reported deal with (i) scientific papers; (ii) resources (documents, videos, links) from a work-integrated learning system; (iii) contributions from a community of knowledge sharing that aggregates Web 2.0 contributions from a range of remote web based services such as Delicious, Youtube, Flickr, Slideshare, and Twitter; (iv) contents from a learning portal for organic agriculture educators; (v) advanced graphical metadata-based access to learning resources in architecture; and (vi) open educational resources. Only the last three focus on educational contents and are relevant from a formal e-learning scenario point of view. Thus, although educational recommender systems (ERS) share the same key objectives as e-commerce recommenders (_i.e._, helping users to select the most appropriate item from a large information pool), there are some particularities that make not possible to directly apply existing solutions from those systems to recommenders for the educational domain. For instance, [PERSON] _et al._ discussed in [8] the particular educational users' tasks and showed that they are different from the typical recommendation tasks that [PERSON] _et al._[28] have described for generic recommender systems. In fact, recommender systems are strongly domain dependent [24], so the characteristics of the educational domain should be taken into account. However, up to now, and as reported in existing reviews of the state of the art (e.g., [8]), ERS are not taking advantage of the semantic descriptions provided by the specifications and standards currently used in the educational domain, such as those compiled in Table 1. Moreover, they mainly focus on recommending learning objects from educational repositories [29] and do not consider the wide range of recommendation opportunities available inLMS.