## 5 Experiments
### Datasets  
To measure the effectiveness of the proposed similarity measure, we relied on an offline analysis approach based on previously collected data from websites [49]. We used two offline datasets: Movie lens (ML-20M) ([[https://grouplens.org/datasets/movielens/20m](https://grouplens.org/datasets/movielens/20m)]([https://grouplens.org/datasets/movielens/20m](https://grouplens.org/datasets/movielens/20m)) (2 February 2021)) [26] and R2-yahoo! Music user ratings of songs with song attributes, version 1.0 datasets ([[https://webscope.sandbox.yahoo.com/catalog.php?datatype=r&](https://webscope.sandbox.yahoo.com/catalog.php?datatype=r&)]([https://webscope.sandbox.yahoo.com/catalog.php?datatype=r&](https://webscope.sandbox.yahoo.com/catalog.php?datatype=r&)) did=2 (15 May 2021)). The ML-20M dataset was collected by the Group Lens Research Team at the University of Minnesota. Each user had at least 20 ratings, and it included two separate files: the first file consisted of (_User id_, _Movie id_, _rating_) and the second file consisted of (_Movie id_, _Movie title_, _Movie genre_). In this dataset, 7120 users participated to rate 27,278 movies and the number of ratings was 1,048,575. Movies were classified into 20 genres. The R2-yahoo! Music dataset represented music preference data for songs from the Yahoo site. It included multiple files: the first file contained (_User id_, _Song id_, rating), the second file included (_Song id_, _Music genre id_), and the last file included (_Genre id_, _Genre name_). We used the data in the \"ydata-ymusic-user-song-ratings-meta-v1_0/train-1.txt\" file. This dataset contained 2829 users, 127,217 songs, and 58 music genres. In both datasets, the files were merged to implement the proposed algorithm. We needed a user id, movie/song id, movie/music genre id, and user rating for the movie/song. Furthermore, we compared our findings to previous researchers' findings by utilizing additional datasets (ML1 and ML-1M). The sparsity level was obtained using the following Equation (5) [50]:  
\[Sparsity=100*\left(1-\frac{\text{all observed ratings}}{m*n}\right) \tag{5}\]  
where \(m\) represents the total number of users, and \(n\) represents the total number of items. In brief, our experiments were based on the first 1500 users who had the highest number of ratings (more than 100 ratings). The dataset details are shown in Table 4.  
Since the proposed algorithm depends on genre information, we describe the genre information in Tables 5 and 6 for the ML-20M dataset and Table 7 for R2-yahoo! Music dataset. Table 5 shows the genre names and the number of movies that belong to them. The drama genre was the most common, and there were 13,344 drama movies. The comedy genre came in second, and there were 8374 movies. Thus, this dataset contained 246 movies belonging to the \"unknown\" genre, which we excluded from the dataset in the experiments. Table 6 shows the number of genres and the number of movies in that genre. The movie may belong to more than one genre. A total of 10,829 movies belonged to a single genre, while 8800 movies belonged to the complex genre (i.e., contained more than one genre). Some movies belonged to five genres. Table 7 shows the genre name in the R2-yahoo music dataset and the number of songs that belongs to it. Most songs have an unknown \"un\" genre, where there are 109,890 \"un\" genre songs. Since we did not use any item from the \"un\" genre in the experiments of the proposed algorithm, the rock genre was first, where there were 7013 songs. In this dataset,