This document summarizes a research paper on personalized top-N sequential recommendation using convolutional sequence embedding. The paper proposes a model called Caser that uses horizontal and vertical convolutional filters to capture sequential patterns at different levels from user behavior data. Caser outperforms previous methods by modeling both general user preferences and sequential patterns in a unified framework. The document provides details on Caser's network architecture, training approach, and evaluation on real-world datasets showing it achieves better performance than baseline methods.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Interactive Recommender Systems with Netflix and SpotifyChris Johnson
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are especially important for interactive recommender systems, including the following: design of the user interface and its tight integration with the algorithm in the back-end; computational efficiency of the recommender algorithm; as well as choosing the right balance between exploiting the feedback from the user as to provide relevant recommendations, and enabling the user to explore the catalog and steer the recommendations in the desired direction.
In particular, we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. We outline some of the user-experiences built, and discuss the approaches followed to tackle the various aspects of interactive recommendations. We present our insights from user studies and A/B tests.
The tutorial targets researchers and practitioners in the field of recommender systems, and will give the participants a unique opportunity to learn about the various aspects of interactive recommender systems in the video and music domain. The tutorial assumes familiarity with the common methods of recommender systems.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Interactive Recommender Systems with Netflix and SpotifyChris Johnson
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are especially important for interactive recommender systems, including the following: design of the user interface and its tight integration with the algorithm in the back-end; computational efficiency of the recommender algorithm; as well as choosing the right balance between exploiting the feedback from the user as to provide relevant recommendations, and enabling the user to explore the catalog and steer the recommendations in the desired direction.
In particular, we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. We outline some of the user-experiences built, and discuss the approaches followed to tackle the various aspects of interactive recommendations. We present our insights from user studies and A/B tests.
The tutorial targets researchers and practitioners in the field of recommender systems, and will give the participants a unique opportunity to learn about the various aspects of interactive recommender systems in the video and music domain. The tutorial assumes familiarity with the common methods of recommender systems.
Déjà Vu: The Importance of Time and Causality in Recommender SystemsJustin Basilico
Talk at RecSys 2017 in Como, Italy on 2017-08-29.
Abstract:
Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the important challenges of handling time in recommendation algorithms at Netflix. We will focus on challenges related to how our users, items, and systems all change over time. We will then discuss some strategies for tackling these challenges, which revolves around proper treatment of causality in our systems.
Tutorial on Deep Learning in Recommender System, Lars summer school 2019Anoop Deoras
I had a fun time giving tutorial on the topic of deep learning in recommender systems at Latin America School on Recommender Systems (LARS) in Fortaleza, Brazil.
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
At Netflix we take context of the member seriously.
In this keynote talk we will see how modeling contextual factors such as time or device can help members to find the right content at the right moment
At the end, the goal is to maximize member satisfaction and retention
These slides will go through which contextual factors matters for the video service and why we choose to use them or not.
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Déjà Vu: The Importance of Time and Causality in Recommender SystemsJustin Basilico
Talk at RecSys 2017 in Como, Italy on 2017-08-29.
Abstract:
Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the important challenges of handling time in recommendation algorithms at Netflix. We will focus on challenges related to how our users, items, and systems all change over time. We will then discuss some strategies for tackling these challenges, which revolves around proper treatment of causality in our systems.
Tutorial on Deep Learning in Recommender System, Lars summer school 2019Anoop Deoras
I had a fun time giving tutorial on the topic of deep learning in recommender systems at Latin America School on Recommender Systems (LARS) in Fortaleza, Brazil.
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
At Netflix we take context of the member seriously.
In this keynote talk we will see how modeling contextual factors such as time or device can help members to find the right content at the right moment
At the end, the goal is to maximize member satisfaction and retention
These slides will go through which contextual factors matters for the video service and why we choose to use them or not.
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
1. January 8, 2020 Page 1/27
Personalized Top-N Sequential Recommendation
via Convolutional Sequence Embedding (WSDM’18)
Jihoo Kim
datartist@hanyang.ac.kr
Dept. of Computer and Software, Hanyang University
Jiaxi Tang, Ke Wang
Simon Fraser University
2. January 8, 2020 Page 2/27
Jiaxi Tang
PhD Student
School of Computing Science
Simon Fraser University
Intern at Google AI
Research & Machine Intelligence Team
Ke Wang
Professor
School of Computing Science
Simon Fraser University
PhD, Georgia Institute of Technology
MS, Georgia Institute of Technology
Recent papers
Towards Neural Mixture Recommender for Long Range Dependent User Sequences (WWW’19)
Jiaxi Tang*, Francois Belletti*, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu and Ed H. Chi
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System (KDD’18)
Jiaxi Tang, Ke Wang
Author
3. January 8, 2020 Page 3/27
Minimum qualifications:
• Currently enrolled in a Master’s or PhD degree in Computer Science or a related technical field.
• Experience (classroom/work) in Natural Language Understanding, Neural Networks, Computer Vision, Machine
Learning, Deep Learning, Algorithmic Foundations of Optimization, Data Science, Data Mining and/or Machine
Intelligence/Artificial Intelligence.
• Experience with one or more general purpose programming languages: Java, C++ or Python.
• Experience with research communities and/or efforts, including having published papers (being listed as author)
at conferences (e.g. NIPS, ICML, ACL, CVPR, etc).
About the job
Research and Machine Intelligence is a high impact team that’s building the next generation of intelligence and
language understanding for all Google products. To achieve this, we’re working on projects that utilize the latest
techniques in Artificial Intelligence, Machine Learning (including Deep Learning approaches like Google AI) and
Natural Language Understanding. We impact products across Google including Search, Maps and Google Now.
https://careers.google.com/jobs/results/136271419680924358-research-intern-2020/
Google AI Research Intern
4. January 8, 2020 Page 4/27
Contents
1. Introduction
1.1 Top-N Sequential Recommendation
1.2 Limitations of Previous Work
1.3 Contributions
2. Related Work
3. Proposed Methodology
3.1 Embedding Look-up
3.2 Convolutional Layers
3.3 Fully-connected Layers
3.4 Network Training
3.5 Recommendation
4. Experiments
4.1 Experimental Setup
4.2 Performance Comparison
4.3 Network Visualization
5. January 8, 2020 Page 5/27
User’s long term
and static behaviors
User’s short term
and dynamic behaviors
General
preferences
Sequential
patterns
<
always
After buying an iPhone, buy phone accessories
“I love Apple’s products”
vs
recent next
<Motivation>
1. Introduction
6. January 8, 2020 Page 6/27
1.1 Top-N Sequential Recommendation
Users
Items
Sequence
order
General preferences
Sequential patterns
Input Output
A list of items
for user u
<Top-N Sequential Recommendation>
<Notations>
1. Introduction
7. January 8, 2020 Page 7/27
1.2 Limitations of Previous Work
<Markov chain based model>
1) FPMC (Factorized Personalized Markov Chains) WWW’10
2) Fossil (Factorized Sequential Prediction with Item Similarity Model) ICDM’16
<Two major limitations>
1) Fail to model union-level* sequential patterns.
2) Fail to allow skip behaviors**.
milk flour
*Union-Level?
butter… …
**Skip behaviors?
… airport hotel
rest-
aurant
bar
attr-
action
not necessary
…
Figure 1
1. Introduction
8. January 8, 2020 Page 8/27
1.2 Limitations of Previous Work
To provide evidences of union-level influences and skip behaviors
minimum support count = 5
minimum confidence = 50%
X Y
sequence
Figure 2
Sequential Association
Rules
→
1. Introduction
9. January 8, 2020 Page 9/27
1.3 Contributions
Caser (ConvolutionAl Sequence Embedding Recommendation Model)
• Caser uses horizontal and vertical convolutional filters to capture sequential patterns
at point-level, union-level, and of skip behaviors.
• Caser models both users’ general preferences and sequential patterns, and
generalizes several existing state-of-the-art methods in a single unified framework.
• Caser outperforms state-of-the-art methods for top-N sequential recommendation on
real life data sets.
1. Introduction
10. January 8, 2020 Page 10/27
• Sequential pattern mining depends on the explicit representation of patterns, thus, could
miss patterns in unobserved states. (= could miss implicit patterns)
• CNN has been used to extract users’ preferences from their reviews. None of these works
is for sequential recommendation.
• RNN was used for session-based recommendation. It may not work well in sequential
recommendation, because not all adjacent actions have dependency relationships.
• Temporal recommendation is related but different problem. (Session-based is also different)
(ex. Recommend coffee in the morning, instead of evening.)
2. Related Work
11. January 8, 2020 Page 11/27
Figure 3
<Network Architecture of Caser>
3. Proposed Methodology
12. January 8, 2020 Page 12/27
The user 𝒖’s sequence
every 𝑳 successive
items
as input
their next 𝑻 items
as the targets
window of
size 𝑳 + 𝑻
The embedding for item 𝒊
d is the number of latent dimensions
𝑺 𝟏
𝒖
𝑺 𝟐
𝒖
𝑺 𝟑
𝒖
𝑺 𝟒
𝒖
𝑺 𝟓
𝒖
𝑬(𝒖,𝟑)
=
𝑸 𝑺 𝟏
𝒖
𝑸 𝑺 𝟐
𝒖
𝑬(𝒖,𝟒) =
𝑸 𝑺 𝟐
𝒖
𝑸 𝑺 𝟑
𝒖
𝑬(𝒖,𝟓) =
𝑸 𝑺 𝟑
𝒖
𝑸 𝑺 𝟒
𝒖
3.1 Embedding Look-up
3. Proposed Methodology
13. January 8, 2020 Page 13/27
image
local features
= 𝑳 × 𝒅 matrix 𝑬
= sequential pattern
Figure 4
Unlike image recognition,
“image” 𝑬 is not given…
and must be learnt
3.2 Convolutional Layers
3. Proposed Methodology
15. January 8, 2020 Page 15/27
activation function
convolutional
sequence embedding
3.3 Fully-connected Layers
the probability of
how likely user 𝒖 will interact
with item 𝒊
at time step 𝒕
3. Proposed Methodology
17. January 8, 2020 Page 17/27
3.4 Network Training
To train the network, we transform the values of the output layers to probabilities
sigmoid function
the collection of the time steps
for which we would like to make
predictions for user 𝒖
the likelihood of all sequences in the dataset
3. Proposed Methodology
18. January 8, 2020 Page 18/27
3.4 Network Training
To further capture skip behaviors, we could consider the next 𝑻 target items
Taking the negative logarithm of likelihood, we get the objective function “binary cross-entropy loss”
model parameters
hyper-parameters
are learned by minimizing the loss function (13)
are tuned on the validation set via grid search
3. Proposed Methodology
19. January 8, 2020 Page 19/27
3.5 Recommendation
After obtaining the trained neural network, to make recommendations for a user 𝒖 at time step 𝒕
We recommend 𝑵 items
that have the highest values
in the output layer 𝒖
𝒖’s last 𝑳 items’
embedding 𝑬(𝒖,𝒕)
𝒖’s latent
embedding 𝑷 𝒖
Input Output
3. Proposed Methodology
20. January 8, 2020 Page 20/27
4.1 Experimental Setup
<Datasets>
Amazon data was not used, due to its SI
0.0026 for ‘Office Products’
0.0019 for ‘Clothing’ / ‘Shoes’ / ‘Jewelry’ / ‘Video Games’
70% 10% 20%
validation testtraining
sequence
4. Experiments
21. January 8, 2020 Page 21/27
<Evaluation Metrics>
4.1 Experimental Setup
MAP(Mean Average Precision): the average of AP for all users
Precision, Recall
top 𝑵 predicted items
for a user
the last 20% of actions
in user’s sequence (= test set)
4. Experiments
23. January 8, 2020 Page 23/27
4.2 Performance Comparison
<Influence of hyper-parameter 𝒅, 𝑳, 𝑻,>
4. Experiments
24. January 8, 2020 Page 24/27
4.2 Performance Comparison
<Analysis of Caser Components>
𝒉 denotes horizontal convolutional layer
𝒗 denotes vertical convolutional layer
𝒑 denotes personalization
Any missing component is represented
by setting its corresponding 𝒐, 𝒐, 𝑷 𝒖 to zero.
4. Experiments
25. January 8, 2020 Page 25/27
4.3 Network Visualization
Caser puts more emphasis on recent actions,
demonstrating a major difference from the conventional top-N recommendation.
<Vertical convolutional filters>
4. Experiments