## 5 Results and Discussion
### Recommendation of Discussion Threads  
As outlined in the Method section, we conducted several preliminary experiments to determine the most useful information from the queries and posts/activities for our recommendation system. We explored the following approaches: (1) calculating the similarity between the title of the query and the body of each post; (2) calculating the similarity between the body of the query and the body of each post; and (3) weighted similarity between the titles and bodies of the query and the post, with double weight given to titles. Our experiments revealed that the weighted similarity between the titles and bodies produced the best results. Thus, we used this approach in our subsequent experiments. Additionally, we compared three different textual representations: bags of words, bags of key phrases, and word embeddings. Table 1 summarizes the evaluation results for the recommendation of similar posts. As expected for all the three approaches, the results of the relaxed evaluation are better than those of the strict evaluation. The best results are obtained when the text is represented as a set of key phrases and the similarity between the query and the post is computed using Jaccard similarity. When using the \"relaxed criterion\" (i.e., when considering that all posts concerning the same algorithmic scheme than the problem stated in the student query are relevant for recommendation), the precision at 5 is above 80%, which means that more than 4 out of the 5 items recommended are relevant to the query. When using the \"strictcriterion\" (i.e., only the posts concerning the same problem and algorithmic scheme than the problem stated in the student query are relevant for recommendation), the precision at 5 decreases to 60%, which means that 3 out of the 5 items recommended are relevant to the query. When the text is represented using bags of words or embeddings, the results are close, but the word embeddings behave better than the bags of words. In the case of the relaxed evaluation strategy, the results of the bags of words and word embeddings approaches are significantly worse than those obtained for the key phrases representation. Our results seem to indicate that, in our particular scenario, key phrases convey the topical content of the text better than individual words. On the one hand, since the names of the different computational problems and algorithmic schemes are not individual words but noun phrases (e.g., \"divide and conquer\", \"branch and bound\", \"binary search\", the skyline problem\", to name a few), this was expected. In contrast, words such as \"search\", \"vector\", \"graph\", and \"heap\" are commonly used when describing different problems and schemes, which may lead to confusion. On the other hand, word embeddings are able to capture contextual information and so produce better results than using words. Figures 2-4 show, in a graphical manner, the evaluation results for the recommendation of similar posts, when the texts are represented as bags of words, key phrases and word embeddings, respectively. We can observe that, when the evaluation is performed under the \"strict\" criterion, the differences between the three approaches are not noticeable. In contrast, in the case of the \"relaxed\" evaluation, the use of the bag of words approach produces poorer results than the other two approaches. We first analyze the results for the \"strict\" evaluation (i.e., we only consider as relevant to a query the posts that address the same problem solved using the same scheme), which are represented by the dashed line in the graphics. First, we see that, for all different textual representations, there are a number of queries for which no relevant posts are recommended (see blue line in Figures 2-4). These queries refer to problems that are underrepresented in the database, with only one or two conversations dealing with them. This is, therefore, a limitation of the dataset that will be progressively overcome as the subject forum grows.