This document summarizes recommender systems, focusing on collaborative filtering techniques. It discusses how recommender systems help with information overload by matching users with relevant items. Collaborative filtering is introduced as a technique that seeks to predict user preferences based on other similar users' ratings. The document then covers various collaborative filtering algorithms like neighborhood models, latent factor models using matrix factorization, and extensions like adding biases and temporal dynamics. It concludes by discussing hybrid methods and providing references for further reading.
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
Anatomy of an eCommerce Search Engine by Mayur DatarNaresh Jain
In this talk, the chief Data scientist of Flipkart will uncover the various challenges in running an e-commerce search platform like scale, recency, update rates, business shaping etc. He will also explain the overall system architecture of the search platform and get into the details of some of the sub-systems, including the query understanding and rewriting sub-system.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Estimating the causal impact of recommender systemsAmit Sharma
Presentation of ACM EC 2015 paper at the International Conference on Computational Social Science.
Link to paper: http://www.amitsharma.in/pubs/ec15_causal_impact_recommendations.pdf
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
Anatomy of an eCommerce Search Engine by Mayur DatarNaresh Jain
In this talk, the chief Data scientist of Flipkart will uncover the various challenges in running an e-commerce search platform like scale, recency, update rates, business shaping etc. He will also explain the overall system architecture of the search platform and get into the details of some of the sub-systems, including the query understanding and rewriting sub-system.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Estimating the causal impact of recommender systemsAmit Sharma
Presentation of ACM EC 2015 paper at the International Conference on Computational Social Science.
Link to paper: http://www.amitsharma.in/pubs/ec15_causal_impact_recommendations.pdf
Recommender systems analyze patterns of user interest in
products to provide personalized recommendations. They seek to predict the rating or preference that user would
give to an item. Some of the most successful realizations of latent factor models are based on matrix factorization...
Text mining to correct missing CRM information: a practical data science projectJonathan Sedar
20min talk given at PyData London 2014
A client in the energy sector wanted to create predictive behavioural models of business customers at the company level, but the CRM data was messy, often containing several sub-accounts for each business, without any grouping identifiers, and so aggregation was impossible. In this talk I describe a short project where we used text mining, a handful of unsupervised learning techniques and pragmatic use of human skill, to identify the true company level structures in the CRM data.
We estimate that nearly one third of news articles contain references to future events. While this information can prove crucial to understanding news stories and how events will develop for a given topic, there is currently no easy way to access this information. We propose a new task to address the problem of retrieving and ranking sentences that contain mentions to future events, which we call ranking related news predictions. In this paper, we formally define this task and propose a learning to rank approach based on 4 classes of features: term similarity, entity-based similarity, topic similarity, and temporal similarity. Through extensive evaluations using a corpus consisting of 1.8 millions news articles and 6,000 manually judged relevance pairs, we show that our approach is able to retrieve a significant number of relevant predictions related to a given topic.
Recommender Systems and Active LearningDain Kaplan
This presentation presents a high level overview of recommender systems and active learning, including from the viewpoint of startups vs. established companies, the cold-start problem, etc.
Online recommendations at scale using matrix factorisationMarcus Ljungblad
This presentation was used for my thesis defense held at Universidad Politecnica de Catalunya, Spain, for a double-degree master programme in Distributed Computing. The other two universities participating in the programme are Royal Institute of Technology, Stockholm, Sweden and Instituto Tecnico Superior, Lisbon, Portugal.
Summary of a Recommender Systems Survey paperChangsung Moon
This is the summary of the following paper:
J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, “Recommender Systems Survey,” Knowledge Based Systems, Vol. 26, 2013, pp. 109-132.
To download please go to: http://www.intelligentmining.com/category/knowledge-base/
Slides as presented by Alex Lin to the NYC Predictive Analytics Meetup group: http://www.meetup.com/NYC-Predictive-Analytics/ on Dec. 10, 2009.
Recommenders Systems tutorial slides from the European Summer School of Information Retrieval (ESSIR).
Covers basic ideas on Collaborative Filtering, Content-based methods, Matrix Factorization, Restricted Boltzmann Machines, Ranking, Diversity.
The slides include material from Xavier Amatriain, Saul Vargas and Linas Baltrunas.
A new similarity measurement based on hellinger distance for collaborating fi...Prabhu Kumar
This project proposed a similarity measurement which is focusing on recommendation performance under the cold start problem [The problem which occurs in the recommendation for newly comer items and users, which doesn't have any recognition in the system] and also perfectly suitable for sparse data set.
This technique solves the problem of the cold start in recommender system as well as improves the performance of recommendation to the users.
Full Tutorial With Pictures: https://www.scienceez.com/build-recommender-system/
Macedonian Computer - Science Faculty (FCSE) Lecture by PhD. Andrea Kulakov. Topic: Recommender Systems
Recommender systems are software tools and techniques providing suggestions for items to be of interest to a user. Recommender systems have proved in recent years to be a valuable means of helping Web users by providing useful and effective recommendations or suggestions.
Data Analyst, Data Scientist, and Data Engineer are three distinct roles within the field of data and analytics, each with its own set of responsibilities and skill requirements. Here's a brief overview of each role:
Recommender Systems from A to Z – The Right DatasetCrossing Minds
In the last years a lot of improvements were done in the field of Machine Learning and the Tools that support the community of developers. But still, implementing a recommender system is very hard.
That is why at Crossing Minds, we decided to create a series of 4 meetups to discuss how to implement a recommender system end-to-end:
Part 1 – The Right Dataset
Part 2 – Model Training
Part 3 – Model Evaluation
Part 4 – Real-Time Deployment
This first meetup will be about building the right dataset and doing all the preprocessing needed to create different models. We will talk about explicit vs implicit feedback, dataset analysis, likes/dislikes vs ratings, users and items features, normalization and similarities.
Slides of my talk I gave at the big data conference: http://www.globalbigdataconference.com/santa-clara/5th-annual-global-big-data-conference/schedule-85.html
Empirical Evaluation of Active Learning in Recommender SystemsUniversity of Bergen
The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor quality data during training. Active learning aims to remedy this problem by focusing on obtaining better quality data that more aptly reflects a user’s preferences. In attempt to do that, an active learning strategy selects the best items to be presented to the user in order to acquire her ratings and hence improve the output of the RS.
In this seminar, I present a set of active learning strategies with different characteristics and the evaluation results with respect to several evaluation measures (i.e., MAE, NDCG, Precision, Coverage, Recommendation Quality, and, Quantity of the acquired ratings and contextual conditions).
The traditional evaluation of active learning strategies has two major flaws: (1) Performance has been evaluated for each user independently (ignoring system-wide improvements) (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition). Addressing these flaws, I present that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system-centric). Hence, I present a novel offline evaluation methodology and use it to evaluate some novel and state of the art rating elicitation strategies.
While the first set of experiments was done offline, the true value of active learning must be evaluated in an online setting. Hence, in the second part of the seminar, I present a novel active learning approach that exploits some additional information of the user (i.e. the user’s personality) to deal with the cold start problem in an up-and-running mobile context-aware RS called STS, that provides users with recommendations for places of interest (POIs). The results of live user studies, have shown that the proposed AL approach significantly increases the quantity of the ratings and contextual conditions acquired from the user as well as the recommendation accuracy.
Similar to Recommender Systems: Advances in Collaborative Filtering (20)
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.”
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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.
5. 5
Information Overload
• Recommender systems help to match users with items
- Ease information overload
- Sales assistance (guidance, advisory, profit increase, ...)
6. 6
Recommender Problem
• Recommender systems are a subclass of information filtering
system that seek to predict the ‘rating’ or ‘preference’ that a user
would give to an item – Wikipedia
8. 8
Data Mining Methods
• Recommender systems
typically apply
techniques and
methodologies of a
genernal data mining
9. 9
Types of Input
• Explicit Feedback
- Feedback that users directly report on their interest in items
- e.g. star ratings for movies
- e.g. thumbs-up/down for TV shows
• Implicit Feedback
- Feedback, which indirectly reflects opinion through observing
user behavior
- e.g. purchase history, browsing history, or search patterns
11. 11
Pros of Collaborative Filtering
• requires minimal knowledge engineering efforts
• needs not consider content of items
• produces good enough results in most cases
• Serendipity of results
12. 12
Challenges for Collaborative Filtering
• Sparsity
- Usually the vast majority of ratings are unknown
- e.g. 99% of ratings are missing in Netflix data
• Scalability
- Nearest neighbor techniques require computation that grows
with both the number of users and the number of items
• Cold Start Problem
- New items and new users can cause the cold-start problem, as
there will be insufficient data for CF to work accurately
13. 13
Challenges for Collaborative Filtering
• Popularity Bias
- tends to recommend popular items
• Synonyms
- Same or very similar items having different names or entries
- Topic modeling like LDA could solve this by grouping
different words belonging to the same topic
• Shilling Attacks
- People may give positive ratings for their own items and
negative ratings for their competitors
14. 14
Content-based Recommendation
• Based on information about item itself, usually keywords or
phrases occurring in the item
• Similarity between two content items is measured by similarity
associated with their term vectors
• User’s profile can be developed by analyzing set of content the
user interacted with
• enables you to compute the similarities between a user and an
item
Similar
15. 15
Pros/Cons of Content-based Approach
• Pros
- No need for data on other users: No cold-start or sparsity
- able to recommend to users with unique tastes
- able to recommend new and unpopular items
- provides explanations by listing content features
• Cons
- In certain domains (e.g., music, blogs and videos), it is
complicated to generate the features for items
- difficult to implement serendipity
- Users only receive recommendations that are very similar
to items they liked or prefered
16. 16
Hybrid Methods
• Weighted
- Outputs from several techniques are combined with different
weights
• Switching
- Depending on situation, the system changes from one
technique to another
• Mixed
- Outputs from several techniques are presented at the same
time
• Cascade
- The output from one technique is used as input of another that
refines the results
17. 17
Hybrid Methods
• Feature Combination
- Features from different recommendation sources are
combined as input to a single technique
• Feature Augmentation
- The output from one technique is used as input features to
another
• Meta-level
- The model learned by one recommender is used as input to
another
18. 18
Two Main Techniques of CF
• Neighborhood Approach
- Relationships between items or between users
• Latent Factor Models
- Transforming both items and users to the same latent factor
space
- Characterizing both items and users on factors inferred from
user feedback
- pLSA
- neural networks
- Latent Dirichlet Allocation
- Matrix factorization (e.g. SVD-based models)
- ...
19. 19
Latent Factor Models
• find features that describe the characteristics of rated objects
• Item characteristics and user preferences are described with
numerical factor values
Action Comedy
20. 20
Latent Factor Models
• Items and users are associated with a factor vector
• Dot-product captures the user’s estimated interest in the item
𝑟𝑢𝑖 = 𝑞𝑖
𝑇
𝑝 𝑢
- Each item i is associated with a vector 𝑞𝑖 ∈ ℝ 𝑓
- Each user u is associated with a vector 𝑝 𝑢 ∈ ℝ 𝑓
• Challenge – How to compute a mapping of items and users to
factor vectors?
• Approaches
- Matrix Factorization Models
- e.g. Singular Value Decomposition (SVD)
21. 21
SVD
• R: 𝑁 × 𝑀 matrix (e.g., N users, M movies)
• U: 𝑁 × 𝑘 matrix (e.g., N users, k factors)
• 𝚺: 𝑘 × 𝑘 diagonal matrix with k largest eigenvalues
• V 𝒕
: 𝑘 × 𝑀 matrix (e.g., k factors, M movies)
24. 24
SVD - Problems
• Conventional SVD has difficulties due to high portion of missing
values in the user-item ratings matrix
• Imputation to fill in missing ratings
- Imputation can be very expensive as it significantly increases
the amount of data
- Inaccurate imputation might distort the data
25. 25
Matrix Factorization for Rating Prediction
• Modeling directly the observed ratings only
𝑚𝑖𝑛 𝑞,𝑝
(𝑢,𝑖)∈𝒦
(𝑟𝑢𝑖 − 𝑞𝑖
𝑇
𝑝 𝑢)2
- 𝒦 is the set of the (u,i) pairs for which 𝑟𝑢𝑖 is known
- 𝑟𝑢𝑖 = 𝑞𝑖
𝑇
𝑝 𝑢
• To learn the factor vectors, 𝑝 𝑢 and 𝑞𝑖 we minimize the squared
error
26. 26
Regularization
• To avoid overfitting through a regularized model
𝑚𝑖𝑛 𝑞,𝑝
(𝑢,𝑖)∈𝒦
(𝑟𝑢𝑖 − 𝑞𝑖
𝑇
𝑝 𝑢)2
+ 𝜆( 𝑞𝑖
2
+ 𝑝 𝑢
2
)
- learn the factor vectors, 𝑝 𝑢 and 𝑞𝑖
- The constant 𝜆, which controls the extent of regularization, is
usually determined by cross validation
- Minimization is typically performed by either stochastic
gradient descent or alternating least squares
Regularization
30. 30
Extended MF (Adding Biases)
• Biases
- Much of the variation in ratings is due to effects associated with
either users or items, independently of their interactions
- i.e., some users tend to give higher ratings than others
- i.e., some items tend to receive higher ratings than others
- A prediction for an unknown rating 𝑟𝑢𝑖 is denoted by 𝑏 𝑢𝑖
𝑏 𝑢𝑖 = 𝜇 + 𝑏𝑖 + 𝑏 𝑢
- 𝜇: the overall average rating over all items
- 𝑏 𝑢 and 𝑏𝑖: the observed deviations of user u and item i
31. 31
Extended MF (Adding Biases)
• Joe tends to rate 0.2 stars lower than the average
• Suppose that the average rating over all movies, 𝜇, is 3.9 stars
• Avengers tends to be rated 0.5 stars above the average
• Avengers movie’s predicted rating by Joe:
𝑏 𝑢𝑖 = 𝜇 + 𝑏𝑖 + 𝑏 𝑢 = 3.9 − 0.2 + 0.5 = 4.2
32. 32
Extended MF (Adding Biases)
• Adding biases
- A rating is created by adding biases
𝑟𝑢𝑖 = 𝜇 + 𝑏𝑖 + 𝑏 𝑢 + 𝑞𝑖
𝑇
𝑝 𝑢
• Objective Function
- In order to learn parameters (𝑏𝑖, 𝑏 𝑢, 𝑞𝑖 and 𝑝 𝑢) we minimize the
regularized squared error
𝑚𝑖𝑛 𝑏,𝑞,𝑝
(𝑢,𝑖)∈𝒦
(𝑟𝑢𝑖 − (𝜇 + 𝑏𝑖 + 𝑏 𝑢 + 𝑞𝑖
𝑇
𝑝 𝑢))2 + 𝜆(𝑏𝑖
2
+ 𝑏 𝑢
2
+ 𝑞𝑖
2 + 𝑝 𝑢
2)
- Minimization is typically performed by either stochastic
gradient descent or alternating least squares
33. 33
Extended MF (Temporal Dynamics)
• Ratings may be affected by temporal effects
- Popularity of an item may change
- User’s identity and preferences may change
• Modeling temporal affects can improve accuracy significantly
• Rating predictions as a function of time
𝑟𝑢𝑖(𝑡) = 𝜇 + 𝑏𝑖(𝑡) + 𝑏 𝑢(𝑡) + 𝑞𝑖
𝑇
𝑝 𝑢(𝑡)
34. 34
SVD++
• Prediction accuracy can be improved by considering also implicit
feedback
• N(u) denotes the set of items for which user u expressed an implicit
preference
• A new set of item factors are necessary, where item i is associated
with 𝑥𝑖 ∈ ℝ 𝑓
• A user is characterized by normalizing the sum of factor vectors:
𝑁(𝑢) −0.5
𝑖∈𝑁(𝑢)
𝑥𝑖
35. 35
SVD++
• Several types of implicit feedback can be simultaneously
introduced into the model
- For example, 𝑁1
(𝑢) is the set of items that the user u rented,
and 𝑁2(𝑢) is the set of items that reflect a different type of
implicit feedback like browsing items
𝑟𝑢𝑖
= 𝜇 + 𝑏𝑖 + 𝑏 𝑢 + 𝑞𝑖
𝑇
𝑝 𝑢 + 𝑁1(𝑢) −0.5
𝑖∈𝑁1
(𝑢)
𝑥𝑖 + 𝑁2(𝑢) −0.5
𝑖∈𝑁2
(𝑢)
𝑥𝑖
37. 37
References
1. Koren, Y. and Bell, R., Advances in collaborative filtering. In
Recommender systems handbook, pp. 145-186, Springer US,
2011
2. Amatriain, X., Jaimes, A., Oliver, N. and Pujol, J.M., Data mining
methods for recommender systems. In Recommender systems
handbook, pp. 39-71, Springer US, 2011
3. Koren, Y., Bell, R. and Volinsky, C., Matrix factorization
techniques for recommender systems. IEEE Computer, (8), pp.
30-37, 2009
4. Dietmar, J. and Gerhard F., Tutorial: Recommender Systems.
Proc. International Joint Conference on Artificial Intelligence
(IJCAI 13), Beijing, 2013
38. 38
References
5. Amatriain, X. and Mobasher, B., The recommender problem
revisited: morning tutorial. In Proceedings of the 20th ACM
SIGKDD international conference on Knowledge discovery and
data mining, pp. 1971-1971, ACM, 2014
6. Bobadilla, J., Ortega, F., Hernando, A. and Gutierrez, A.,
Recommender systems survey. Knowledge-Based Systems, 46,
pp. 109-132, 2013
7. Moon, C., Recommender systems survey. SlideShare, 2014
(http://www.slideshare.net/ChangsungMoon/summary-of-rs-
survey-ver-07-20140915)
8. Freitag, M and Schwarz, J., Matrix factorization techniques for
recommender systems. Presentation Slides in Hasso Plattner
Institut, 2011
(http://hpi.de/fileadmin/user_upload/fachgebiete/naumann/leh
re/SS2011/Collaborative_Filtering/pres1-
matrixfactorization.pdf)