A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks
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Transcript of A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks
![Page 1: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/1.jpg)
@cataldomusto @ale_suglia
@cld_greco @SWAP_research
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural NetworksALESSANDRO SUGLIA, CLAUDIO GRECO, CATALDO MUSTO, MARCO DE GEMMIS, PASQUALE
LOPS, GIOVANNI SEMERARO
UNIVERSITÀ DEGLI STUDI DI BARI ‘ALDO MORO’ - ITALY
25th International Conference on User Modeling, Adaptation and Personalization
Bratislava, SlovakiaJuly 12, 2017
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Recurrent Neural Networks (RNNs)Widespread Deep Learning Architecture◦ Based on Neural Networks
◦ The connections between the units may contain loops which let consider past states in the learning process
◦ Very suitable to model variable-length sequential data
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 3: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/3.jpg)
Recurrent Neural Networks (RNNs)Widespread Deep Learning Architecture◦ Based on Neural Networks
◦ The connections between the units may contain loops which let consider past states in the learning process
◦ Very suitable to model variable-length sequential data
PROS CONS
◦ Very good performance in different tasks
◦ Can learn short-term and long-term (temporal) dependencies
◦ Vanishing/exploding gradient problem
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 4: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/4.jpg)
Recurrent Neural Networks (RNNs)Widespread Deep Learning Architecture◦ Based on Neural Networks
◦ The connections between the units may contain loops which let consider past states in the learning process
◦ Very suitable to model variable-length sequential data
PROS CONS
◦ Very good performance in different tasks
◦ Can learn short-term and long-term (temporal) dependencies
◦ Vanishing/exploding gradient problem
LONG-SHORT TERM MEMORY NETWORKS (LSTMS)◦ Introduced to solve the vanishing/exploding gradient problem
Each cell presents a complex structure which is more powerful than simple RNN cells.
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 5: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/5.jpg)
Motivations
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
?
In content-based recommender systems
suggestions are generated by matching
the features stored in the user profile
with those describing the items to be
recommended
![Page 6: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/6.jpg)
Motivations
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
user profile
?
items
In content-based recommender systems
suggestions are generated by matching
the features stored in the user profile
with those describing the items to be
recommended
![Page 7: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/7.jpg)
Motivations
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
user profile
?
items
In content-based recommender systems
suggestions are generated by matching
the features stored in the user profile
with those describing the items to be
recommended
Content Representation plays a key role!
![Page 8: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/8.jpg)
Motivations
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
user profile
?
items
In content-based recommender systems
suggestions are generated by matching
the features stored in the user profile
with those describing the items to be
recommended
RNNs are very suitable!Content can be considered as a
sequence of terms
![Page 9: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/9.jpg)
Research Question
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 10: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/10.jpg)
Research Question
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
Our contribution
AMAR (Ask Me Any Rating)Deep Architecture inspired by a neural
network model used to solve Question
Answering toy tasks [*]
[*] J. Weston et al. “Towards AI-Complete Question
Answering: A Set of Prerequisite Toy Tasks”.
In: CoRR abs/1502.05698 (2015)
![Page 11: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/11.jpg)
Research Question
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
Our contribution
AMAR (Ask Me Any Rating)Deep Architecture inspired by a neural
network model used to solve Question
Answering toy tasks [*]
[*] J. Weston et al. “Towards AI-Complete Question
Answering: A Set of Prerequisite Toy Tasks”.
In: CoRR abs/1502.05698 (2015)
AnalogyQuestion:Answers = User Profile:Items
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AMAR: Ask Me Any Rating
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 13: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/13.jpg)
AMAR: Ask Me Any RatingUser and Item are modeled through two embeddings
EMBEDDINGS ARE JOINTLY LEARNED
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 14: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/14.jpg)
AMAR: Ask Me Any RatingUser and Item are modeled through two embeddings
EMBEDDINGS ARE JOINTLY LEARNED
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
Given an item, its textual description w1 , ... ,wn isrepresented through a RNN with LSTM cells
Each LSTM generates a latent representation h(wi) for each word wi
The final representation of the item is obtainedthrough a MEAN POOLING LAYER
![Page 15: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/15.jpg)
AMAR: Ask Me Any RatingUser and Item are modeled through two embeddings
EMBEDDINGS ARE JOINTLY LEARNED
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
The resulting embeddings are merged through a CONCATENATION LAYER
Given an item, its textual description w1 , ... ,wn isrepresented through a RNN with LSTM cells
Each LSTM generates a latent representation h(wi) for each word wi
The final representation of the item is obtainedthrough a MEAN POOLING LAYER
![Page 16: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/16.jpg)
AMAR: Ask Me Any RatingUser and Item are modeled through two embeddings
EMBEDDINGS ARE JOINTLY LEARNED
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
A LOGISTIC REGRESSION LAYER estimates user interest in the item and builds the recommendation list.
Given an item, its textual description w1 , ... ,wn isrepresented through a RNN with LSTM cells
Each LSTM generates a latent representation h(wi) for each word wi
The final representation of the item is obtainedthrough a MEAN POOLING LAYER
The resulting embeddings are merged through a CONCATENATION LAYER
![Page 17: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/17.jpg)
AMAR+AMAR has a very modular and extensiblearchitecture
It is possible to add extra modules to encodemore information beyond the simple descriptionof the item
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 18: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/18.jpg)
AMAR+AMAR has a very modular and extensiblearchitecture
It is possible to add extra modules to encodemore information beyond the simple descriptionof the item
AMAR+ introduces A GENRE EMBEDDING,whichrepresents the genre associated to the item to be recommended
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 19: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/19.jpg)
AMAR+AMAR has a very modular and extensiblearchitecture
It is possible to add extra modules to encodemore information beyond the simple descriptionof the item
AMAR+ introduces A GENRE EMBEDDING,whichrepresents the genre associated to the item to be recommended
For each genre g1, … , gm associated to an item a genre embedding is learnt. All the embeddingsare averaged through a MEAN POOLING LAYER.
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 20: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/20.jpg)
AMAR+AMAR has a very modular and extensiblearchitecture
It is possible to add extra modules to encodemore information beyond the simple descriptionof the item
AMAR+ introduces A GENRE EMBEDDING,whichrepresents the genre associated to the item to be recommended
For each genre g1, … , gm associated to an item a genre embedding is learnt. All the embeddingsare averaged through a MEAN POOLING LAYER.
The new information is merged and the pipeline estimates again the user preference in the item
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 21: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/21.jpg)
Experiments
How does our deep architectureperform when compared to other
content-based recommendersystems or state-of-the-art
baselines?
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 22: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/22.jpg)
Datasets
MovieLens 1M (ML1M)
6,040 users3,883 movies1,000,209 ratings57.51% positive ratings165.59 ratings/user (avg.)269.88 ratings/item (avg.)99.4% sparsity
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 23: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/23.jpg)
Datasets
DBbook
6,181 users6,733 movies72,732 ratings45.86% positive ratings11.71 ratings/user (avg.)10.74 ratings/item (avg.)99.8% sparsity
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 24: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/24.jpg)
Experimental SettingsTop-N recommendation task
Metric◦ F1@5
AMAR parameters◦ RMSprop optimizer, 25 epochs
◦ a=0.9, learning rate 0.001
◦ Batch size 1536 (ML1M) and 512 (DBbook)
◦ Binary cross entropy as cost function
◦ User, Item and Genre embedding size = 10
Item Processing◦ Mapping item names with Wikipedia pages
◦ Extraction of textual content from plots
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 25: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/25.jpg)
BaselinesWord Embedding techniques
◦ Word2Vec
◦ Glove
◦ Doc2Vec
◦ In Word2Vec and Glove, items/profile are representedas the centroid vector of the representation of the word occurring in the textual descriptions
Collaborative Filtering and Matrix Factorizationtechniques
U2U-CF, I2I-CF
BPRMF, BPRSlim, WRMF
Optimal parameters. All available in MyMediaLite toolkit
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
![Page 26: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/26.jpg)
BaselinesWord Embedding techniques
◦ Word2Vec
◦ Glove
◦ Doc2Vec
◦ In Word2Vec and Glove, items/profile are representedas the centroid vector of the representation of the word occurring in the textual descriptions
Collaborative Filtering and Matrix Factorizationtechniques
◦ U2U-CF, I2I-CF
◦ BPRMF[*], BPRSlim[+], WRMF
◦ Optimal parameters.
◦ All available in MyMediaLite toolkit
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
[*] S. Rendle, C.Freudenthaler, Z. Gantner, L. Schmidt-Thieme:
BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009.
[+] X. Ning, G. Karypis: Slim: Sparse linear methods for top-n recommender systems. ICDM 2011.
![Page 27: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/27.jpg)
Results – MovieLens data
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
0.5550.558
0.490.482 0.485
0.427 0.431 0.425 0.423
0.446
MovieLens
AMAR AMAR+ Word2Vec Doc2Vec Glove U2U I2I BPRMF WRMF BPRSlim
![Page 28: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/28.jpg)
0.5550.558
0.490.482 0.485
0.427 0.431 0.425 0.423
0.446
MovieLens
AMAR AMAR+ Word2Vec Doc2Vec Glove U2U I2I BPRMF WRMF BPRSlim
Results – MovieLens data
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
Word
Embeddingtechniques
![Page 29: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/29.jpg)
0.5550.558
0.490.482 0.485
0.427 0.431 0.425 0.423
0.446
MovieLens
AMAR AMAR+ Word2Vec Doc2Vec Glove U2U I2I BPRMF WRMF BPRSlim
Results – MovieLens data
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
Word
Embeddingtechniques
Collaborative Filtering and
Matrix Factorization
techniques
![Page 30: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/30.jpg)
Results – DBbook data
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
0.5640.565
0.542 0.540.552
0.536 0.536
0.5080.519
0.511
MovieLens
AMAR AMAR+ Word2Vec Doc2Vec Glove U2U I2I BPRMF WRMF BPRSlim
![Page 31: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks](https://reader034.fdocuments.in/reader034/viewer/2022042611/5a654dd97f8b9ace0b8b49e7/html5/thumbnails/31.jpg)
0.5640.565
0.542 0.540.552
0.536 0.536
0.5080.519
0.511
MovieLens
AMAR AMAR+ Word2Vec Doc2Vec Glove U2U I2I BPRMF WRMF BPRSlim
Results – DBbook data
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
AMAR and AMAR+
overcome all the
baselines
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RecapAMAR: a deep architecture for content-based recommendation exploiting RNNs
◦ Neural Network predicts the likelihood that a user would like a certain item
◦ User and Item embeddings are jointly learned.
◦ LSTMs to model textual description of the items.
Results
AMAR and AMAR+ significantly improve all the baselines
Modular and Extensible Architecture: AMAR+ introduces a genre embedding
High training time (ML1M=90’ per epoch , DBbook=50’ per epoch)
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
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Thanks!
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
@cataldomusto, @ale_suglia
@cld_greco, @SWAP_research