## 2 Related Work  
Recommender systems are used in a wide variety of applications, such as recommending books, music, movies, or news. In general, a recommender system is a software designed to recommend things to the user based on her preferences and needs [13]. There are two broad types of recommender systems [14]: collaborative filtering and content-based. Collaborative recommenders use the opinions and choices of users with similar profiles, along with the information about the user's past behavior, to predict the items the user will be interested in. The main disadvantage of this approach is the so-called cold-start problem that emerges when new items are added to the catalog and have either none or very few interactions. In contrast, content-based recommenders make use of a list of attributes or features that describe the items in the catalog to recommend items that are similar to others that were liked or chosen by the user in the past. This approach has the main advantage of user independence, so that it does not need the existence of a network of users and their ratings to infer the recommendations, but it has the main problem of over-specialization and provides a limit degree of novelty. A third category of recommenders follows a hybrid approach, which combines methods from both collaborative and content-based systems. Recommenders are usually employed in e-learning environments to support personalized learning by recommending resources and learning activities according to the student's preferences, motivation and needs. They may help access the huge amount of learning resources that are available in the different repositories, so that only relevant information is presented to the student. An important number of recommender systems have been deployed in the last decade to serve in e-learning settings. Although the design principles are similar to those of other recommenders (e.g., music recommenders) the information retrieval goals are often different. The most popular application of recommenders in education aims to help students in the selection of their courses [15; 16; 17; 18; 19]. Another important category of recommenders is directed to propose reinforcement activities to students that experience any type of difficulty, or to recommend extension materials for further study [20; 21; 22]. Finally, other works attempt to suggest instructors or course designers' materials for improving the course [23; 24]. In recent times, the utilization of recommendation systems in online forums has garnered considerable interest. [PERSON] et al. [25] propose the recommendation of threads similar to a given one, rather than solely relying on the user's query. To achieve this, they conceptualize threads as a collection of posts and establish similarity using diverse strategies grounded in basic lexical matching. While this approach bears merit, it doesn't fully address our particular scenario, wherein the challenge is to offer assistance to students prior to receiving a response from either the instructor or fellow students. [PERSON] and [PERSON] [26] leverage the thread's structure to smooth the language model of a post, utilizing the context of the thread containing it, addressing issues such as posts containing incomplete information due to the assumption that prior posts have already been read. [PERSON] et al. [27] present an approach to identify forum posts that are relevant to a specific post. They achieve this by segmenting the text into sections and then calculating the similarity between these segments. A key aspect of their work is the introduction of a segmentation method based on the concept of \"communication mean\", which ensures coherence and accurate segmentation of passages. The authors assess their methodology using hotel review and travel review forums. It is noteworthy to highlight that while their study focuses on the recommendation of opinions, our current challenge involves a more complex scenario. In our case, the recommended posts must provide concrete solutions to specific problems.