The document discusses several algorithms for predicting which music artists a user has listened to:
1) A content-based algorithm uses a linear regression model to predict listens based on song metadata genres and a user's preferences for those genres. However, the model is impossible to solve due to insufficient data.
2) A collaborative filtering approach constructs imaginary metadata and learns user and song profiles to predict listens, but ignores similarities between users.
3) K-nearest neighbors fills in missing listens for a user based on the average listens of similar users' nearest neighbors.
CIPD Guernsey Channel Islands Discrimination Law Update SlidesRichard Sheldon
The Ghosts of Past, Present and Future
As 2016 is fast drawing to a close we thought now would be an ideal time to reflect on what has been a busy past couple of years in the Channel Islands in relation to equal opportunities and discrimination law with our policy advisor Richard Sheldon.
In a short space of time, the landscape around equalities legislation has radically changed and the drive to bring us closer to the position in the UK over the next few years seems inevitable.
Whilst the laws may differ between Guernsey and Jersey, the approach of each island inevitably influences the other on matters of policy, especially with so many businesses now operating on a pan-island basis, so the session will cover developments across both including:
A look at the past discrimination cases across both islands including the first discrimination decision in Jersey;
A look at the present policy thinking following introduction of new maternity rights in Guernsey and age discrimination in Jersey this year; and
A look to the future of discrimination laws across both islands with disability discrimination in both islands and the how employers will tackle retirement in Jersey come 2018.
Mary Gallert is an author who has written a number of novels, which she hopes to get published at some point in the near-future. Her creative works often focus on the subject of adoption, placing particular focus on the struggles that children face when growing up with the knowledge that they are adopted. She hopes that her work will help other people with the issues that she herself faced when she was younger, particularly those related to rejection and abandonment.
Tambi studios is one of the leading film, video and post production facilities in the Middle East. We offer international production companies and advertising agencies a ‘one stop shop’ in film production.
http://www.tambistudios.com/
Ejemplo de PROGRAMA DE EXPLORACIÓN GEOTÉCNICA - MTY N.L. MÉX.Raúl OS
UNIVERSIDAD AUTÓNOMA DE SINALOA.
FACULTAD DE INGENIERÍA CULIACÁN.
LICENCIATURA EN INGENIERÍA CIVIL.
INGENIERÍA EN CIMENTACIONES
Ing. SANDRA SÁNCHEZ SANDOVAL.
PROGRAMA DE EXPLORACIÓN GEOTÉCNICA EN CENTRO DE LA CIUDAD DE MONTERREY NUEVO LEÓN, MÉXICO.
PRESENTAN: Duarte Calleros Erick Adolfo, Sanguino Ramos Raúl Omar. (Estudiantes de ingeniería Civil).
Cln. Rosales, Sin, a 19 de Febrero de 2016.
CIPD Guernsey Channel Islands Discrimination Law Update SlidesRichard Sheldon
The Ghosts of Past, Present and Future
As 2016 is fast drawing to a close we thought now would be an ideal time to reflect on what has been a busy past couple of years in the Channel Islands in relation to equal opportunities and discrimination law with our policy advisor Richard Sheldon.
In a short space of time, the landscape around equalities legislation has radically changed and the drive to bring us closer to the position in the UK over the next few years seems inevitable.
Whilst the laws may differ between Guernsey and Jersey, the approach of each island inevitably influences the other on matters of policy, especially with so many businesses now operating on a pan-island basis, so the session will cover developments across both including:
A look at the past discrimination cases across both islands including the first discrimination decision in Jersey;
A look at the present policy thinking following introduction of new maternity rights in Guernsey and age discrimination in Jersey this year; and
A look to the future of discrimination laws across both islands with disability discrimination in both islands and the how employers will tackle retirement in Jersey come 2018.
Mary Gallert is an author who has written a number of novels, which she hopes to get published at some point in the near-future. Her creative works often focus on the subject of adoption, placing particular focus on the struggles that children face when growing up with the knowledge that they are adopted. She hopes that her work will help other people with the issues that she herself faced when she was younger, particularly those related to rejection and abandonment.
Tambi studios is one of the leading film, video and post production facilities in the Middle East. We offer international production companies and advertising agencies a ‘one stop shop’ in film production.
http://www.tambistudios.com/
Ejemplo de PROGRAMA DE EXPLORACIÓN GEOTÉCNICA - MTY N.L. MÉX.Raúl OS
UNIVERSIDAD AUTÓNOMA DE SINALOA.
FACULTAD DE INGENIERÍA CULIACÁN.
LICENCIATURA EN INGENIERÍA CIVIL.
INGENIERÍA EN CIMENTACIONES
Ing. SANDRA SÁNCHEZ SANDOVAL.
PROGRAMA DE EXPLORACIÓN GEOTÉCNICA EN CENTRO DE LA CIUDAD DE MONTERREY NUEVO LEÓN, MÉXICO.
PRESENTAN: Duarte Calleros Erick Adolfo, Sanguino Ramos Raúl Omar. (Estudiantes de ingeniería Civil).
Cln. Rosales, Sin, a 19 de Febrero de 2016.
Metric Learning for Music Discovery with Source and Target PlaylistsYing-Shu Kuo
Playlist generation for music exploration by defining sets of source songs and target songs and deriving a playlist through metric learning and boundary constraints.
https://github.com/hank5925/mlmdstp
Walk the Talk: Analyzing the relation between implicit and explicit feedback ...Denis Parra Santander
Presentation at UMAP back in July 2011. This paper obtained 2nd best student paper award. Joint work with Xavier Amatrian.
You can download the paper here:
http://www.sis.pitt.edu/~dparra/Walk_the_talk_NS.pdf
Metric Learning for Music Discovery with Source and Target PlaylistsYing-Shu Kuo
Playlist generation for music exploration by defining sets of source songs and target songs and deriving a playlist through metric learning and boundary constraints.
https://github.com/hank5925/mlmdstp
Walk the Talk: Analyzing the relation between implicit and explicit feedback ...Denis Parra Santander
Presentation at UMAP back in July 2011. This paper obtained 2nd best student paper award. Joint work with Xavier Amatrian.
You can download the paper here:
http://www.sis.pitt.edu/~dparra/Walk_the_talk_NS.pdf
Walk the Talk: Analyzing the relation between implicit and explicit feedback ...
GreenMonster
1. Predict which music artists a user has listened
to
Yangjun Wang, Yang Zhao
Department of Information and Computer Science
Aalto University, School of Science and Technology
yangjun.wang@aalto.fi, yang.zhao@aalto.fi
2. Predict which music artists a user has listened to
May 12, 2014
2/13
Algorithms we tried
Content based algorithm
Collaborative filtering
KNN
3. Predict which music artists a user has listened to
May 12, 2014
3/13
Content based algorithm
Main Idea
There are various genres in music, such as jazz, rock, pop...
A user has a special preference to certain genres (θ)
Each song has weights of being different genres (x) namely,
the metadata.
Based on this, we can build a linear regression model
y = θT
x
where y represents how may times the user has listened to this
song.
4. Predict which music artists a user has listened to
May 12, 2014
4/13
Content based algorithm
Example
The weights(x) of a song is Xi = (0.8, 0.2, 0)T .
A user’s preference to these three genres is Θi = (10, 0, 0)T .
The probability that this user has listened the artist would be
yi = θT
i xi = 8
So this user has listened to this song for 8 times.
5. Predict which music artists a user has listened to
May 12, 2014
5/13
Content based algorithm
More details
How do we do with missing values?
We ignore them when we are computing the error
The complete model is
E =
i
(θT
i xi − yi )
2
ri
where ri = 1 if that value is not missing.
6. Predict which music artists a user has listened to
May 12, 2014
6/13
Content based algorithm
Result
Does this work?
We don’t know.
Because in metadata, each song has more than 10k weights
But we have only 4k observations for each user
It’s impossible to solve these equations
7. Predict which music artists a user has listened to
May 12, 2014
7/13
Collaborative filtering
Main idea
Genres provided in metadata may be too much
Maybe none of those song belongs to that genre
Why don’t we construct an imaginary metadata?
8. Predict which music artists a user has listened to
May 12, 2014
8/13
Collaborative filtering
Implementation
Randomize m k-dimensional vector. k is the number of genres
we suppose these songs will have. m is number of songs.
Randomize n k-dimensional vector. Each vector is a
preference for a user. n stands for the number of users.
Compute gradients:
∂E
∂Θ
,
∂E
∂X
Update Θ, X separately.
Check if error function converges.
9. Predict which music artists a user has listened to
May 12, 2014
9/13
Collaborative filtering
More details
To prevent over-fitting, we also used regularization factors in
error function.
Since the final submission requires us to submit 0,1 data, we
have to set a threshold
So we have to set 3 magic numbers, k: number of genres, λ:
regularization coefficient, t: threshold value
Since we know the ration of 0 vs 1 is 4:6, we set the threshold
to let the final prediction has this ratio.
10. Predict which music artists a user has listened to
May 12, 2014
10/13
Collaborative filtering
Comments
This method builds a profile for each user, it doesn’t consider
the similarity between users.
The error function focus on global error, namely it assigns
equal importance to every observations. However, it’s
different if a user listened to a song for 100 times from 1 time.
There are so many zeros in the training data. That is a strong
interfere. This method fits prediction like movie ratings better,
namely, rating can only be 1-5 stars. In other words, when the
rating can not be zero and the scale of the rating is similar.
11. Predict which music artists a user has listened to
May 12, 2014
11/13
Collaborative filtering
Result
12. Predict which music artists a user has listened to
May 12, 2014
12/13
Logistic Regression
It is similar with Linear Regression.
The cost function and gradient is different.
y =
1
1 + e−θT x
13. Predict which music artists a user has listened to
May 12, 2014
13/13
KNN
Denotation
Y : the given user-artists matrix (Y(Y>1)=1)
K : the size of neighbors set
Main idea
For each row r of Y, get the K-nearest neighbors(ignore the
missing variables).
Set the missing variables in r as the average of the
corresponding variables(same column) in its K-neighbors.