Merge branch 'master' of https://github.com/lucasresck/espotifai

report/docs/baseline_model/model.md

@@ -1,6 +1,8 @@
 # Baseline model: Random Walk
 
-## Based on the article [Timo van Niedek & Arjen P. de Vries](https://dl.acm.org/doi/pdf/10.1145/3267471.3267483)
+## Based on the article [Niedek and Vries](https://dl.acm.org/doi/pdf/10.1145/3267471.3267483)
+
+We develop a simpler model, called baseline model, in order to compare our algorithms.
 
 The ideia is to represent the playlist dataset as a bipartite graph. We use multiple random walks over it. The playlist title are used for prefiltering and ranking titles. This is the simplest way to use similarity between tracks. 
 

report/docs/conclusion.md

@@ -1,9 +1,8 @@
 # Conclusion
 
-In this work we presented two similarity-based metohos for automatic playlist
-continuation and baseline model for comparison. 
-We developed inspered on RecSys Challenge 2018 and other works
-
+In this work we presented two similarity-based methods for automatic playlist
+continuation and a baseline model for comparison. 
+We developed them inspired by the RecSys Challenge 2018 and other works
 on the topic. The dataset used was generated by the Spotify API and the models
 were developed using *Python*. 
 
@@ -13,14 +12,14 @@ special we can have multiple lines and columns, as long as many spaces are
 zero.
 
 In the baseline model, we did a random walk with uniform probability in the
-sparse matrix, with probability of restart the process being a Geometric
-Distribution with parameter $\alpha$. In the first model, we found that with small start playlists the
+sparse matrix, with probability of restart the process being a geometric
+distribution with parameter $\alpha$. In the first model, we found that with small start playlists the
 algorithm 
 outperforms, what is a good result. However this model is not much scalable
 and some changes must be made, like a prefiltering. In the second model we 
 found that the sparse matrix takes little time to be created and the model
 acchieved a reasonable performance compared to the models from the RecSys
-Challenge 2018.
+Challenge 2018. We saw that our models outperform the baseline model.
 
 Some directions we could follow for future works is combining different
 algorithms, *hybrid algorithms*. Other interesting thing to do is to

report/docs/evolution.md

@@ -12,7 +12,7 @@ There's no homogeneity in research community about playlist data. We saw that ea
 
 Last.fm is a social network about music. Using the package pyLast, we gathered data from Last.fm users, in a network process fashion: starting with a few users, we walked through their followers recursively. At the end, we had public information about many users, tracks and artists.
 
-Spotify is a music streaming service. Using the package Spotipy and the Spotify Web API we scrapped playlist data from many users. Because Spotify doesn't allow us to request user followers, we couldn't gather the data as with Pylast. So we tested the Last.fm users and we colleted their public playlists if available. At the end, he had many Spotify public playlists, as well as information about the tracks, such as the audio features, and about the artists too.
+Spotify is a music streaming service. Using the package Spotipy and the Spotify Web API we scrapped playlist data from many users. Because Spotify doesn't allow us to request user followers, we couldn't gather the data as with pyLast. So we tested the Last.fm users and we colleted their public playlists if available. At the end, he had many Spotify public playlists, as well as information about the tracks, such as the audio features, and about the artists too.
 
 ## Exploratory data analysis
 

report/docs/index.md

@@ -13,11 +13,10 @@ We know nowadays recommenders are extremely important, both for the greater prod
 We studied and implemented two algorithms that try to solve the problem of
 playlist continuation, inspired by [Kelen et
 al.](https://dl.acm.org/doi/10.1145/3267471.3267477) and [Pauws and
-Eggen](http://ismir2002.ircam.fr/proceedings/OKPROC02-FP07-4.pdf). Both of
-them use an idea of Nearest Neighbors, but the former use a similarity among playlists and
-the latter use a similarity among tracks. The baseline model was inspired by [
-Timo  Van Niedek & Arjen P. de
-Vries](https://dl.acm.org/doi/10.1145/3267471.3267483), with random walk with
+Eggen](http://ismir2002.ircam.fr/proceedings/OKPROC02-FP07-4.pdf).
+The former use a similarity among playlists, and
+the latter use a similarity among tracks. The baseline model was inspired by
+[Niedek and Vries](https://dl.acm.org/doi/10.1145/3267471.3267483), with random walk with
 restart. 
 
 ## Motivation

report/docs/related_work.md

@@ -20,9 +20,8 @@ participant groups of the ACM RecSys Challenge 2018, [Kelen et
 al.](https://dl.acm.org/doi/10.1145/3267471.3267477) (2018) inspired us in our
 playlist-based similarity algorithm. [Bonnin and
 Jannach](https://dl.acm.org/doi/10.1145/2652481) (2014) gave us an
-introduction and an overview of the playlist continuation problem. Also was [A
-Large-Scale Evaluation of Acoustic and Subjective Music Similarity
-Measures](https://www.ee.columbia.edu/~dpwe/pubs/ismir03-sim.pdf) (2003). The
-baseline was based on the Random Walk based algorithm from [
-Timo  Van Niedek & Arjen P. de Vries](https://dl.acm.org/citation.cfm?id=3267483)
+introduction and an overview of the playlist continuation problem. Also was
+[Berenzweig et al.](https://www.ee.columbia.edu/~dpwe/pubs/ismir03-sim.pdf) (2003). The
+baseline was based on the Random Walk based algorithm from
+[Niedek and Vries](https://dl.acm.org/citation.cfm?id=3267483) (2018).
 

report/mkdocs.yml

@@ -5,9 +5,9 @@ nav:
     - Related work: related_work.md
     - Project evolution: evolution.md
     - EDA: eda/eda.md
-    - Baseline model: baseline_model/model.md
     - Model based on track similarity: model_1/model.md
     - Model based on playlist similarity: model_2/model.md
+    - Baseline model: baseline_model/model.md
     - Conclusion: conclusion.md
 
 repo_url: https://github.com/lucasresck/espotifai