Collaborative Recommender System for Music using Pytorch. Combine Matrix Factorization and Neural Networks for improved performance. Python sample code included.
Collaborative Recommender System for Music using Pytorch. Combine Matrix Factorization and Neural Networks for improved performance. Python sample code included.
Smart Data Slides: Machine Learning - Case StudiesDATAVERSITY
The state of the art and practice for machine learning (ML) has matured rapidly in the past 3 years, making it an ideal time to take a look at what works and what doesn’t.
In this webinar, we will review case studies from 3 industries:
-Insurance
-Healthcare
-Pharma
Participants will learn to look for characteristics of business processes and of data that make them well - or ill - suited to augmentation or automation with ML.
Every researcher is a cyborg! Academic researchers engage various sorts of research in vitro (in the glass) and in vivo (in the living body), or they engage in experimental laboratory work and analyze data in natural in-world experiments. In between, many conduct surveys, focus groups, interviews, and other types of research work. In the computer-assisted qualitative data analysis software (CAQDAS) space, NVivo is one of the foremost tools, enabling the creation of manual codebooks, multimedia analysis, and various forms of “auto” or unsupervised machine learning. NVivo works as a “database” for structured and unstructured data (multimedia). It enables the drawing of content from various social media sites. Technologies augment human analytical capabilities, in the qualitative and quantitative research spaces. This presentation demonstrates some of the capabilities of NVivo. This also addresses how a researcher is changed by the computational capabilities they harness.
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
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows.
Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
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.
Deliver Dynamic Customer Journey Orchestration at ScaleDatabricks
As the customer acquisition costs are rising steadily, organizations are looking into ways to optimize their end-to-end customer experience in order to convert prospects into customers quickly and to retain them for a longer period of time.
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.
We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. We used data from Movielens website. Collaborative filtering and matrix factorization techniques have been used for the implementation. The end result is a web application where a user is recommended with top 20 movies.
Codebase: http://goo.gl/nM7RMy
Demo Video: http://goo.gl/VgZ2uI
Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
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.
Algorithmic Music Recommendations at SpotifyChris Johnson
In this presentation I introduce various Machine Learning methods that we utilize for music recommendations and discovery at Spotify. Specifically, I focus on Implicit Matrix Factorization for Collaborative Filtering, how to implement a small scale version using python, numpy, and scipy, as well as how to scale up to 20 Million users and 24 Million songs using Hadoop and Spark.
Smart Data Slides: Machine Learning - Case StudiesDATAVERSITY
The state of the art and practice for machine learning (ML) has matured rapidly in the past 3 years, making it an ideal time to take a look at what works and what doesn’t.
In this webinar, we will review case studies from 3 industries:
-Insurance
-Healthcare
-Pharma
Participants will learn to look for characteristics of business processes and of data that make them well - or ill - suited to augmentation or automation with ML.
Every researcher is a cyborg! Academic researchers engage various sorts of research in vitro (in the glass) and in vivo (in the living body), or they engage in experimental laboratory work and analyze data in natural in-world experiments. In between, many conduct surveys, focus groups, interviews, and other types of research work. In the computer-assisted qualitative data analysis software (CAQDAS) space, NVivo is one of the foremost tools, enabling the creation of manual codebooks, multimedia analysis, and various forms of “auto” or unsupervised machine learning. NVivo works as a “database” for structured and unstructured data (multimedia). It enables the drawing of content from various social media sites. Technologies augment human analytical capabilities, in the qualitative and quantitative research spaces. This presentation demonstrates some of the capabilities of NVivo. This also addresses how a researcher is changed by the computational capabilities they harness.
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
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows.
Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
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.
Deliver Dynamic Customer Journey Orchestration at ScaleDatabricks
As the customer acquisition costs are rising steadily, organizations are looking into ways to optimize their end-to-end customer experience in order to convert prospects into customers quickly and to retain them for a longer period of time.
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.
We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. We used data from Movielens website. Collaborative filtering and matrix factorization techniques have been used for the implementation. The end result is a web application where a user is recommended with top 20 movies.
Codebase: http://goo.gl/nM7RMy
Demo Video: http://goo.gl/VgZ2uI
Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
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.
Algorithmic Music Recommendations at SpotifyChris Johnson
In this presentation I introduce various Machine Learning methods that we utilize for music recommendations and discovery at Spotify. Specifically, I focus on Implicit Matrix Factorization for Collaborative Filtering, how to implement a small scale version using python, numpy, and scipy, as well as how to scale up to 20 Million users and 24 Million songs using Hadoop and Spark.
Analysis, design and implementation of a Multi-Criteria Recommender System ba...Davide Giannico
"Analysis, design and implementation of a Multi-Criteria Recommender System based on Aspect Extraction and Sentiment Analysis techniques" is my final work presentation for the Master of Computer Science at University of Bari "Aldo Moro"(Italy).
This work mainly discusses two algorithms of multi-criteria recommendation based on the extracted information from the item reviews using Aspect Extraction and Sentiment Analysis techniques.
The user usually doesn't read all the reviews correlated to each item, ignoring a lot of useful information. This happens because that analysis takes a lot of time and energy.
The main opportunity for us has been to take advantage of the reviews informative power, incorporating such information in the recommendation process.
Moreover the existing systems usually use a default taxonomies of criteria on which the user can express his rate.The most of the times these criteria are not exhaustive respect to user preferences and vague (not specific). Differently our approach is based on these three steps: Automatic identification of criteria from the reviews using Aspect Extraction techniques, Sentiment Analysis for associating a preference level to the extracted criteria (implicit rating) and Extension of multi-criteria item-based recommendation algorithms exploiting the information extracted.
Both the algorithms introduces the multi-criteria component in the item-to-item matrix calculus. The Aspect extraction and Sentiment Analysis step has been possible using an other system named ORE (Opinion Original Engine) who belongs to the Department of Computer Science (University of Bari "Aldo Moro").
The first algorithm named #Multi-ORE-criteria uses the multi-ORE-ratings for calculating the similarity item-to-item matrix using several mutli-criteria similarity metrics (Pearson, Euclidean e.g.)
The second algorithm named #MARTA (Multi-criteria Aspect-based Recommender system based on sentimenT Analysis ) uses each item description for calculating the similarity item-to-item matrix using several mutli-criteria similarity metrics (Pearson, Euclidean e.g.). The item description format consists of several (A,S) pairs where A is the extracted aspect and S is its related score. This step has been possible using Aspect Extraction and Sentiment Analysis techniques on the reviews dataset.
The implementation of both is based on Mahout Machine Learning library (http://mahout.apache.org/), extending the Item-Based Recommendation algorithms.
In the last part we discuss about the experimentation, results, conclusions and futur work.
Solving the AL Chicken-and-Egg Corpus and Model ProblemDain Kaplan
Active learning (AL) is often used in corpus construction (CC) for selecting “informative” documents for annotation. This is ideal for focusing annotation efforts, but has the limitation that it is carried out in a closed-loop manner, selecting points that will improve an existing model. When there is no model, or the task(s) is even under-defined (such as studying corpora-less phenomena), use of traditional AL is inapplicable. To remedy this, we propose a novel method for model-free AL that focuses on utilising phenomena as desir- able characteristics. We introduce a tool, MOVE, that helps iteratively visualise and refine these characteristics. We show its potential on a real world case-study of a corpus we are developing.
Your own recommendation engine with neo4j and reco4php - DPC16Christophe Willemsen
Graph Databases are naturally well-suited for building recommendation engines. In this talk, Christophe will share his experience building a number of production-ready recommendation engines using Neo4j and introduce the open-source GraphAware Reco4PHP Library, which enables PHP developers to rapidly build their own recommender systems.
This presentation starts by a brief explanation of why graphs are a suitable data model for building recommender systems. A summary of typical recommendation engine requirements follows, including the business and technical challenges these requirements introduce. Afterwards, the talk dives into possible solutions of these challenges, both from business and architectural/design perspectives, and introduces the GraphAware Reco4PHP Library.
What follows is a demonstration of how this open-source recommendation engine skeleton solves many of the issues and how it handles the "plumbing", so that developers can focus on expressing the business logic specific to their domain.
A majority of examples in this talk are drawn from real-world use cases and the speaker's personal experience building recommendation engines. Attendees should have a very basic understanding of graph theory. Prior experience with Neo4j and the Cypher query language is a plus, but not necessary.
Attendees will learn:
* what is a recommendation engine and what it is good for
* why graphs are a good fit for building one
* what business and technical challenges one faces building a recommender
* what possible solutions there are for these challenges
* how to build a high-performance graph-based recommendation engine in minutes
* real-world case studies
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.
Profile injection attack detection in recommender systemASHISH PANNU
Recommender System are backbone of e-commerce websites. But now a days we can not relay 100% on the performance of RS. Attackers can affect the results of recommender system. It become very important for service provider to reduce the impact of attacks.
The Recommendation Engine is a tool which provides the various users a chance to buy different things and check what is in trend or what is liked by most of the people by going through the recommendations given to them on the basis of their past searches and other people’s buying history.
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.
Recommendation Engine Powered by Hadoop - Pranab GhoshBigDataCloud
Personalized recommendations are ubiquitous in social network and shopping sites these days. How do they do it? As long as enough user interaction data is available for items e.g., products in shopping sites, a kind of recommendation engine based on what’s known as ' Collaborative Filtering' is not that difficult to build. Since the solution causes a combinatorial explosion, Hadoop can play a critical role in processing massive amount of data in collaborative filtering based solutions. In this presentations, I will cover a Hadoop based recommendation engine implementation using collaborative filtering.
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.
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
User Preferences Based Recommendation System for Services using Mapreduce App...IJMTST Journal
Service recommendations based on the user preferences using keyword aware service recommendation
system simply called as KASR. Here the keyword shows the preference of the user. Based on the keyword
service, recommendations are provided for the user. For this process we use a user-based collaborative
filtering algorithm. To improve the efficiency of this process we implement KASR in Hadoop environment
which is a open-source software framework for storing data and running applications on clusters of
commodity hardware. It provides massive storage for any kind of data, enormous processing power and the
ability to handle virtually limitless concurrent tasks or jobs. To improve the efficiency and scalability of the
KASR we proposed the combined preferences using rank boosting algorithm. In the rank boosting
algorithm, it gets the input as combined preferences, based on the preferences it process the similarities
with the reviews of the existing users then it provides the ranking to the services. Based on the ranking
provided to the services we generate the output recommendations with high similarity matching results as
the recommendation list to the end users for their combined preferences.
The slides from the Machine Learning Summers School 2015 in Sydney on Machine Learning for Recommender Systems. Collaborative filtering algorithms, Context-aware methods, Restricted Boltzmann Machines, Recurrent Neural Networks, Tensor Factorization, etc.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
The Internet, which brought the most innovative
improvement on information society, web recommendation
systems based on web usage mining try to mine user’s behavior
patters from web access logs, and recommend pages or
suggestions to the user by matching the user’s browsing behavior
with the mined historical behavior patterns. In this paper we
propose a recommendation framework that considers different
application status and various contexts of each user. We
successfully implemented the proposed framework and show how
this system can improve the overall quality of web
recommendations.
I
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Monitoring Java Application Security with JDK Tools and JFR Events
Multi Criteria Recommender Systems - Overview
1. Diversity in Recommender System
How to extend SINGLE-CRITERIA RecommenderSystems ?
Author :
DAVIDEGIANNICO
Specialists formanaging information systems basedon the semantic manipulation of information -
University of Bari
Multi-Criteria Recommender Systems
2. Outline
• Introduction to RECOMMENDERSYSTEMS
•Introduction to MULTI-CRITERIARECOMMENDER SYSTEMS(MCRS)
•MCRS :TYPOLOGIES & Some recentworks
•OPENISSUES AND CHALLENGES
Specialists formanaging information systems basedon the semantic manipulation of information -
University of Bari
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
4. RECOMMENDER SYSTEMS are a SOLUTION to
the InformationOverload…
We need a INTELLIGENT Information Access
We need a way to FILTER the information
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
5. Which RECOMMENDATIONTECHNIQUES do
we have ? (1/2)
COLLABORATIVEFILTERING
CONTENT-BASED
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
6. HYBRID
KNOWLEDGE-BASED
Which RECOMMENDATIONTECHNIQUES do
we have ? (2/2)
Knowledge
A
B
C
Recommend
Model
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
7. Are theCLASSICAL RECOMMENDATION
techniquesPERFECT?!
Single-criteriamovieRS Multi-criteriamovieRS
7 8
7 8
Story : 5
Actors : 9
Story : 9
Actors : 7
Story : 8
Actors : 6
Story : 7
Actors : 9
(atypicalexample)
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
A
B
A
B
8. RECOMMENDATIONas a MULTI-CRITERIA
DECISION MAKING PROBLEM
Bernard Roy’s (pioneer inMCDM) METHODOLOGY:
1. Definethe object of decision
2. Defininga consistent familyof criteria
3. Developinga global preference model
4. Selectionof thedecision support process
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
9. CLASSIFICATIONof MCRS*
MCRS
Decision
Problematic
Types of criteria
Global preference
model approach
*AccordingtotheMCDM framework
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
Chooice
Ranking
Sorting
Description
Measurable
Ordinal
Probabilistic
Fuzzy
Value Focused Model
Multi Objective Optimization Model
Outranking relation model
Preference disaggregation model
11. MULTI CRITERIA RATING–BASED PREFERENCE
ELICITATION
WHERE could we USE that information?
5
5
6
7
7
6
5
6
7
7
6
9
5
??? ?7 7
Star Wars Fargo Toy Story Saw
•PREDICTIONPHASE
•RECOMMENDATIONPHASE
6
65 9
95
5 7 ? 7 ? 7 ? 7 ?
5 7 5 7 9 5 6 9 5
6 6 6 6 5 6 5 9 6
? ? ? ?
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
12. MULTI-RATINGRS –anEXAMPLE
Single-criteriamovie
Recommender Systems
Multi-criteria movie
Recommender Systems
5,2,2,8,8 7,5,5,9,9 5,2,2,8,8 7,5,5,9,9
5,8,8,2,2 7,9,9,5,5 5,8,8,2,2 7,8,8,2,2
6,3,3,9,9 6,4,4,8,8 6,3,3,9,9 6,4,4,8,8
? Reting to be
predicting
Reting to be
using in
prediction
Reting to be
predicting
Reting to be
using in
prediction
5 7 5 7 ?
5 7 5 7 9
6 6 6 6 5
?
9
5,2,2,8,8 7,5,5,9,9 5,2,2,8,8 7,5,5,9,9 ?,?,?,?,?
5,8,8,2,2 7,9,9,5,5 5,8,8,2,2 7,8,8,2,2 9,8,8,10,
10
6,3,3,9,9 6,4,4,8,8 6,3,3,9,9 6,4,4,8,8 5,2,2,8,8
?,?,?,
?,?
5,2,2,
8,8
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
A
B
C
A
B
C
13. Prediction -phase: HEURISTIC-BASED(1/3)
• NEIGHBORHOOD-BASED collaborative filtering recommendation (context)
Similarity computation method in single-rating : correlation-base &cosine-based
Person correlation-based Cosine-based
HOW TOEXTEND THISTO MULTI-CRITERIA?
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
14. Prediction-phase : HEURISTIC-BASED(2/3)
Two approaches :
1.Aggregation of traditional similarities that arebased on each individual criteria
a. Calculate similarity between two users separately on each indidual
criterion;
b. Final similarity between two users is obtained by aggregating
individual similarity values. How?
I.
II.
(Adomavicius)
(Adomavicius)
III. (Tang an McCalla)
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
15. Two approaches :
2.Calculate similarity using multidimensional distance metrics
a. Calculate distance between two users u eu’on item i
I.
II.
III.
b. Calculate overall distance between two users
I.
Prediction-phase : HEURISTIC-BASED(3/3)
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
16. Do they workBETTER?
Empirical results using the small-scale Yahoo! Movies dataset show that BOTH HEURISTIC APPROACHES
OUTPERFORM thecorresponding traditional single-rating collaborative filtering technique byup 3.8% in
terms of precision-in-top-Nmertric.
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
17. Aggregation function
Itfinds r0 = f(r1,..,rk)relation btw overall and multi-criteriaratings.
Step 1.Estimate k individual ratings using any raccomandation tecnique.
Step 2.f is choosen using domain expertize, statistical tecniques (linear
regression) or machinelearningtechnique.
Step 3. Overall rating of each unrateditem is computed based on the k
predicted individual criteria ratingand the choosen aggregation function f.
up 0.3-6.8%in terms
of precision-in-top-N
mertric.
(Yahoo Movies)
Prediction-phase : MODEL-BASED (1/2)
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
PERFORMANCE
18. Other Approaches:
•Probabilstic Modeling Approach (Sahoo et all.)
(Yahoo Movies!; Precision/Recall-in-top-Nmertric -maximum of 10%increase)
•Multi singular value decomposition(MSVD) approach (Li et all.)
(Collaborative filtering; context of restaurant recommendersystems, Precision-in-top-Nmertric - maxiumumof
5% increase).
Prediction-phase : MODEL-BASED(2/2)
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
19. Recommendation-phase
When overall ratings are included as partof the model , theraccomandation process is very
straightforward, essentially the same as in single-criteria RS.
Without an overall rating the recommandation process becomes more complex.
Approaches for Multi-criteria optimization :
- Finding Pareto optimal solutions;
- …..
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
20. Using Multi-Criteria ratings as RECOMMENDATION
FILTERS
Multi-criteria ratings can be used as recommendation filters in RS.
Story: 8
Actors: 7
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
Story:9;Actors:10
Story:8;Actors:8
Story:10;Actors:7
21. DATASET
• Yahoo Movies!
• Trip Advisor
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
22. FRAMEWORK
• Single-rating
• Multi-rating: NO ONE!
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
23. OPEN ISSUES & CHALLENGES
• Managing Intrusivness
• Reusingexisting single-rating
recommendationstechnique
• Costructing theitemevaluation criteria
• Dealing with missing multi-criteriaratings
• Developing newMCDMmodeling approach
• Collecting large-scalemulti criteriaratingdata
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari
24. REFERENCES
• AccuracyImprovementsforMulti-CriteriaRecommenderSystems(DietmarJ., ZeynepK.,FatihG.)
• Multi-CriteriaUserModeling in RecommenderSystems(KleanthiL.,NikolaosF., Alexis T.)
• Multi CriteriaRecommenderSystems(Adomavicius,Manouselis,Kwon)
• NewRecommendationTechniques forMulti-CriteriaRatingSystems(Adomavicius,Kwon)
Multi-Criteria Recommender Systems - Specialists formanaging information systems
based on the semantic manipulation of information -University of Bari