This is a tutorial about recommender system for CS410 @ UIUC. It summarize some good research paper about how user profile and tags can improve recommender systems.
This is a tutorial about recommender system for CS410 @ UIUC. It summarize some good research paper about how user profile and tags can improve 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.
talk at KTH 14 May 2014 about matrix factorization, different latent and neighborhood models, graphs and energy diffusion for recommender systems, as well as what makes good/bad recommendations.
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.
A tutorial on the query auto-completion, with more than 10 conference papers, about the development of current query auto-completion and its personalized, time-sensitive , mobile features and so on.
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.
talk at KTH 14 May 2014 about matrix factorization, different latent and neighborhood models, graphs and energy diffusion for recommender systems, as well as what makes good/bad recommendations.
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.
A tutorial on the query auto-completion, with more than 10 conference papers, about the development of current query auto-completion and its personalized, time-sensitive , mobile features and so on.
PhD defense : Multi-points of view semantic enrichment of folksonomiesFreddy Limpens
This thesis, set at the crossroads of Social Web and Semantic Web, is an attempt to bridge Social tagging-based systems with structured representations such as thesauri or ontologies (in the informatics sense). Folksonomies resulting from the use of social tagging systems suffer from a lack of precision that hinders their potentials to retrieve or exchange information. This thesis proposes supporting the use of folksonomies with formal languages and ontologies from the Semantic Web. Automatic processing of tags allows bootstraping the process by using a combination of a custom method analyzing tags' labels and adapted methods analyzing the structure of folksonomies. The contributions of users are described thanks to our model SRTag, which allows supporting diverging points of view, and captured thanks to our user friendly interface allowing the users to structure tags while searching the folksonomy. Conflicts between individual points of view are detected, solved, and then exploited to help a referent user maintain a global and coherent structuring of the folksonomy, which is in return used to garanty the coherence while enriching individual contributions with the others' contributions. The result of our method allows enhancing the navigation within tag-based knowledge systems, but can also serve as a basis for building thesauri fed by a truly bottom up process.
Crafting Recommenders: the Shallow and the Deep of it! Sudeep Das, Ph.D.
I present a brief review, and an outlook on the rapid changes happening in the field of recommendation engine research on the heels of the deep learning revolution!
Generating domain specific sentiment lexicons using the Web Directory acijjournal
In this paper we aim at proposing a method to automatically build a sentiment lexicon which is domain based. There has been a demand for the construction of generated and labeled sentiment lexicon. For data on the social web (E.g., tweets), methods which make use of the synonymy relation don't work well, as we completely ignore the significance of terms belonging to specific domains. Here we propose to
generate a sentiment lexicon for any domain specified, using a twofold method. First we build sentiment scores using the micro-blogging data, and then we use these scores on the ontological structure provided by Open Directory Project [1], to build a custom sentiment lexicon for analyzing domain specific microblogging data.
Improving Semantic Search Using Query Log AnalysisStuart Wrigley
Despite the attention Semantic Search is continuously gaining, several challenges affecting tool performance and user experience remain unsolved. Among these are: matching user terms with the searchspace, adopting view-based interfaces in the Open Web as well as supporting users while building their queries. This paper proposes an approach to move a step forward towards tackling these challenges by creating models of usage of Linked Data concepts and properties extracted from semantic query logs as a source of collaborative knowledge. We use two sets of query logs from the USEWOD workshops to create our models and show the potential of using them in the mentioned areas.
In this talk we will explain some of the main challenges that we faced at OLX Europe while trying to proof the value of a deep learning based recommender system, and to later productionize it with a high level of automation.
We'll talk about:
* Modern Recommender Systems
* Deep Learning
* Neural Item Embeddings
* Similarity Search
* Proving value through Experimentation
* From POC to PRD
* Lessons Learned
About the speakers:
Cristian Martinez works as Lead Data Scientist at OLX Group, mainly focused on Search and Recommenders, and has been working for more than a decade in different companies solving business problems with Machine Learning.
Ilia Ivanov is a Data Scientist in OLX Europe (online marketplace) with 4 years of experience in DS focusing on recommendations and NLP.
2017 10-10 (netflix ml platform meetup) learning item and user representation...Ed Chi
Learning item and user representations with sparse data in recommender systems
Ed H. Chi
Google Inc.
Recommenders match users in a particular context with the best personalized items that they will engage with. The problem is that users have shifting item and topic preferences, and give sparse feedback over time (or no-feedback at all). Contexts shift from interaction-to-interaction at various time scales (seconds to minutes to days). Learning about users and items is hard because of noisy and sparse labels, and the user/item set changes rapidly and is large and long-tailed. Given the enormity of the problem, it is a wonder that we learn anything at all about our items and users.
In this talk, I will outline some research at Google to tackle the sparsity problem. First, I will summarize some work on focused learning, which suggests that learning about subsets of the data requires tuning the parameters for estimating the missing unobserved entries. Second, we utilize joint feature factorization to impute possible user affinity to freshly-uploaded items, and employ hashing-based techniques to perform extremely fast similarity scoring on a large item catalog, while controlling variance. This approach is currently serving a ~1TB model on production traffic using distributed TensorFlow Serving, demonstrating that our techniques work in practice. I will conclude with some remarks on possible future directions.
A lecture on evaluating AR interfaces, from the graduate course on Augmented Reality, taught by Mark Billinghurst from the HIT Lab NZ at the University of Canterbury.
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...Gabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling. Then, we present a case of how we've combined those techniques to build Smart Canvas, a SaaS that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to a content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
Short-Bio: Gabriel Moreira is a scientist passionate about solving problems with data. He is Head of Machine Learning at CI&T and Doctoral student at Instituto Tecnológico de Aeronáutica - ITA. where he has also got his Masters on Science. His current research interests are recommender systems and deep learning.
https://www.meetup.com/pt-BR/machine-learning-big-data-engenharia/events/239037949/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
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.
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.
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.
How world-class product teams are winning in the AI era by CEO and Founder, P...
Tag And Tag Based Recommender
1. Tag & Tag-based Recommenders
IBM Research – China
Presenter: Xiatian Zhang (张夏天)
Team:
赵石顽 张夏天 袁 泉
2. About Me
2000-2004, B.S. Math, Central South University
2004-2007, M.S. Computer Science, BUPT
2007-Present, Researcher, Working on Recommender Systems and
Data Mining
3. Agenda
Social Tagging System and Its Features
Tag Recommender
Tag-based Recommender
4. Social Tagging
A folksonomy is a system of classification derived from the practice
and method of collaboratively creating and managing tags to annotate
and categorize content; this practice is also known as collaborative
tagging, social classification, social indexing, and social tagging.
Folksonomy is a portmaneau of folk and taxonomy.
Social Tagging boomed from 2004, with the wave of Web 2.0.
– Delicious
– Citeulike
– Bibsonomy
– Youtube
– Flickr
– Dogear – A internal social book marking system in IBM
– …
5. Some Insights of Tagging System
Shilad Sen et.al., tagging, communities, vocabulary, evolution,
CSCW’06
– Modeling vocabulary evolution
– Tagging system features
– Based on Movielens recommender system
– Personal tendency and community influence
– Tag displaying strategies and their effects
– Tag utility
7. Tagging System Features
Design Features
– Tag Sharing
– Tag Selection
– Item Ownership
– Tag Scope
– Broad
– Narrow
Tag Class
– Factual Tag
– Subjective Tag
– Personal Tag
9. Personal Tendency
How strongly do investment and
habit affect personal tagging
behavior?
– 1. Habit and investment
influence user’s tag applications.
– 2. Habit and investment
influence grows stronger as
users apply more tags.
– 3. Habit and investment cannot
be the only factors thatcontribute
to vocabulary evolution.
10. Community Influence
How does the tagging
community influence
personal vocabulary?
– 1. Community influence
affects a user’s personal
vocabulary.
– 2. Community influence
on a user’s first tag is
stronger for users who
have seen more tags.
13. Tag Recommender
Purpose
– Encourage users to tag more frequently, apply more tags to an
individual resource, reuse common tags
– Make user use tags not previously considered.
– Eliminate Redundant tags
– Promote a core tag vocabulary steering the user toward adopting
certain tags while not imposing any strict rules.
– Avoid ambiguous tags in favor of tags that offer greater information
value.
14. Tag Recommender – Technologies
Naive Methods
– Most Popular Tags on Resources
– Most Popular Tags on Users
– Most Popular Tags on Resources and Users
Classical Collaborative Filtering
– User-KNN
– Item-KNN
Adapted KNN Methods
– Extend User-Item Matrix
– Degrade User-Item-Tag Relationship
Content-based Method
Tensor Method
– Tensor Factorization
Graph Based
– FolkRank
Our Work
16. Adapted KNN – Degrade User-Item-Tag relationship
Process
– TF/IDF on UI, UT, IT
– P-Core Processing
– Remove noise data
– Extract User Model by
Hebbian Deflation
18. FolkRank
PageRank
PR( p j )
PR( pi ) (1 d ) / N d
p j M ( pi ) L( p j ) (1)
Personalized PageRank
PR( p j )
PR( pi ) (1 d ) pi d
p j M ( pi ) L( p j ) (2)
FolkRank
1. Compute global PageRank by (1)
2. Then for each <user, item> pair, compute personalized PageRank by (2)
– p[i] = 1, but p [u] = 1 + |U| and p [r] = 1 + |R|.
3. FolkRank = Personalized PageRank - PageRank
19. Our Work
Explored and Exploring Methods
– Non-classical Tensor Fusion Factorization
– Multi-label Classification by Random Decision Trees, High Speed
– The performance of both two methods are close to FolkRank
Current Progress
– Shiwan develop a simple graph model
– Best precision and recall on several datasets compared to other
methods
– We are writing paper targeting ACM RecSys 2010
20. Tag-based Recommender
Our Work
– IUI 2008 Paper, Improved Recommendation based on Collaborative
Tagging Behaviors
– Explored Methods
– Tensor Factorization
– Non-classical Tensor and Matrix Fusion Factorization
Other Works
– Shilad Sen, Jesse Vig, and John Riedl, Tagommenders: Connecting
Users to Items through Tags, WWW 2009
21. IUI 2008 Paper Overview
We invent a new collaborative filtering approach TBCF (Tag-based Collaborative
Filtering) based on the semantic distance among tags assigned by different users
to improve the effectiveness of neighbor selection.
That is, two users could be considered similar not only if they rated the items
similarly, but also if they have similar cognitions over these items.
Example
– Both Bob and Tom may rate the movie Avatar with 5 stars, which indicates they
all like this movie very much.
– Nevertheless, as a 3D fan, Bob appreciates this movie for its high quality 3D
animations, while Tom may think that it is a wonderful action movie.
22. Tag-based Collaborative Filtering
Tag-based User-Item Matrix
Item1 Item2 Item3 Item4
Alice Art, photo Home, Products Writing, Design Learning,
Education
Daniel Photo, Album, Ø Typewriter Tutorial, Training
Image
Sherry Ø Cleaning Ø Language, Study
Maggie Photography Ø Ovens Ø
Steps
1. Calculate the semantic similarity of tags based on WordNet (for the tags not
included in WordNet, calculate the edit-distance instead)
2. Calculate the similarity between tag sets
3. Calculate the similarity between user u and v by summing up the similarity of tag
sets on common pages (tagged by both u & v)
4. Find the top-N nearest neighbors of the active user to make the prediction
5. Return the top-M predicted items to the active user
23. Tag Similarity Calculation
Tag similarity
– WordNet
– LSA/PLSA
Tag set similarity
– Hungarian method
WordNet Concept Tree
Word similarity in WordNet
If x and y are contained in WordNet, dis(x,y) is the shortest path length between x and y.
24. Experimental Evaluation
Data Set
Extract total 8000 users, 5315 pages and 7670 tags from web logs.
Algorithm Average Precision Average Ranking
TBCF 0.27 2.8
cosine 0.13 1.5
Random generated subset Average Precision Average Precision
TBCF cosine
500 0.208 0.121
2000 0.182 0.118
4000 0.202 0.173
6000 0.209 0.180