Coupling Everything: A Universal Guideline for Building State-of-The-Art Recommender Systems

  • Introduction (overview, challenges, preliminary)
  • RS with User couplings
  • RS with Item couplings
  • RS with Multifold couplings
  • RS with Comprehensive couplings
  • Conclusion remarks
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1. Coupling Everything: A Universal Guideline for Building State-of-The-Art Recommender Systems Presenter: Liang Hu, Shoujin Wang and Longbing Cao Authors: Liang Hu, Shoujin Wang, Songlei Jian, Longbing Cao, Jian Cao

2.Related tutorial • Non-IID Learning of Complex Data and Behaviors (T14) • Non-iid recommender systems • Non-iid customer behaviors • Non-idd relations • Non-iid learning methods • …… • Time : 14:00-18:00, Sunday 11, August 2019 • Venue: L • Presenter: Prof. Longbing Cao

3.Special issues on recommender systems • IEEE Intelligent Systems Special issue: Intelligent Recommendation with Advanced AI and Learning • Due on September 30, 2019. • Link: https://sites.google.com/view/shoujin-wang/home/activities/intelligent- recommendation-with-advanced-ai-and-learning • International Journal of Data Science and Analytics (JDSA) Special issue: Data Science for Next-Generation Recommender Systems • Due on November 30, 2019. • Link: https://sites.google.com/view/shoujin-wang/home/activities/jdsa- special-issue

4.Goal Providing a comprehensive understanding of how to apply the state-of-the- art machine learning approaches to model the universal couplings in the advanced recommender systems.

5.Agenda • Introduction (overview, challenges, preliminary) (45 mins) • RS with User couplings (45 mins + 30 mins break) • RS with Item couplings (45 mins) • RS with Multifold couplings (45 mins) • RS with Comprehensive couplings (45 mins) • Conclusion remarks (10 mins)

6. Outline Learning Learning Learning Learning Introduction user item multifold comprehensi Conclusion couplings couplings couplings ve couplings • Overview • Social RS • Cross-domain • Multi-modal • Context- • Conclusion • Challenges • Group RS RS RS aware RS • Summary • Preliminaries • Session-based • Multi-criteria • Fashion AI in RS RS RS • Heterogenous couplings in RS

7. Outline Learning Learning Introduction • Overview user of recommender item Learning systems multifold Learning comprehensi Conclusion couplings couplings couplings ve couplings • Challenges of recommender systems • Couplings in recommender system • Overview • Social RS • Cross-domain • Multi-modal • Context- • Conclusion • Challenges •• Group AI-related RS preliminaries: RS from RS data representation aware RS perspective • Summary • Session-based • Preliminaries • RepresentingRS attributes • Multi-criteria • Fashion AI in RS RS • Representing review • Heterogenous • Representing rating table couplings in RS • Representing image • Representing network • Representing sequence

8.What Are Recommender Systems • Recommender systems (push information) are the evolution of information retrieval systems (pull information). Information Age Recommendation Age Pull mode (IRS): Query Matched Results Manual Filtering Push mode (RS): Potential Requirement Machine Filtering Recommendation Anderson, C. (2006). The long tail: Why the future of business is selling less of more

9. Recommender Systems have occupied our life What to eat Which to dress Where to go

10.Personalized Recommendation

11.Classic Recommender Systems Recommender Systems Collaborative Content-based Demographic Knowledge- Filtering Filtering RS based RS User-based Item-based

12. Collaborative Filtering (CF) • Intuition (user-based filtering): If user A A x related to user B and A bought x and y, then B bought x tend to buy y. • Famous examples(item-based filtering): Amazon.com's recommender system B y • Facebook, MySpace, LinkedIn use collaborative filtering to recommend new friends, groups, and other social connections. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Paper presented at the Proceedings of the 10th international conference on World Wide Web, Hong Kong, Hong Kong.

13.Content-based Filtering (CBF) • CBF is based on the features of items • Attributes of items • Description of items • Text of an article User Profile • User profile is built with the features of historical items • Recommend items according to user profile

14.Data Characteristics in Recommender Systems • Power law or Long tail distribution • Data associated with the majority of users are insufficient and even absent in real world. • In most recommender systems, the majority of users/items only associated with very few data while only minority of users/items have sufficient data

15.Challenges in Collaborative Filtering • Data Sparsity • In real-world recommender systems, the … user-item matrix is very sparse. • Cold Start 1 4 5 3 • When new users or new items are added, the system cannot recommend to these … 1 2 users and these items. 5 • Scalability 5 • There are millions of users and products in real systems. • Large amount of computation • Large storage

16.Challenges in Content-based Filtering • Limited Content Analysis • System has a limited amount of information on its users or the content of its items. • Over-specialization • The system can only recommend items that highly similar with user’s profile, the user is limited to be recommended items similar to those already rated.

17.Question: what’s the main cause of these challenges? • Data Sparsity • Cold Start Insufficiency of data • Limited Content Analysis • Over-specialization

18.Data complexity challenges existing theories and systems What Ad Irrelevant and would Damaging to Brand you place here?

19.Coupling in Complex Data • Coupling relationships: • Within and between values, attributes, objects, sources, aspects, … • Structures, distributions, relations, … • Methods, models, … • Outcomes, impact, … Longbing Cao. Non-IIDness Learning in Behavioral and Social Data, The Computer Journal, 57(9): 1358-1370 (2014).

20. Address the RS from multi-coupling perspectives A. Coupling on users: • Social RS: user mutual influence • Group RS: group joint decision B. Coupling on items: • Cross-domain RS: domain coupling • Session-based RS: temporal coupling C. Coupling on implicit interaction: • Context-aware RS: contextual dependency • Multi-objective RS: multi-aspect ratings • Attraction RS: subjective attention Cao, L. (2016). Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting. Engineering, 2(2), 212-224.

21.Coupling modeling in advanced RS • Coupling modeling : • The coupling between users social RS, group-based RS • The coupling between items session-based RS, cross-domain RS • The coupling between data types multi-modal RS • The coupling between domains cross-domain RS • The coupling between objectives multi-objective RS

22. Universal coupling modeling in this tutorial Universal coupling RS User coupling Item coupling Multifold coupling Cross- Session- Multimodal Multi- Social RS Group RS domain RS based RS RS objective RS

23. Outline Learning Learning Introduction • Overview user of recommender item Learning systems multifold Learning comprehensi Conclusion couplings couplings couplings ve couplings • Challenges of recommender systems • Couplings in recommender system • Overview • Social RS • Cross-domain • Multi-modal • Context- • Conclusion • Challenges •• Group AI-related RS preliminaries: RS from RS data representation aware RS perspective • Summary • Session-based • Preliminaries • RepresentingRS attributes • Multi-criteria • Fashion AI in RS RS • Representing review • Heterogenous • Representing rating table couplings in RS • Representing image • Representing network • Representing sequence

24.AI, Machine Learning and Deep Learning

25.Machine Learning Methods Dominate RS Competitions Alibaba Competitions

26.Prospects: modeling RS with more complex data • Built on More Complex Data • Multiple data types • Ratings • Images Data complexity • Text • Multisource • Multiple domains • Multiple systems • Social data • Acquire data from user social media • Multiple criteria • Multi-objectives: accuracy, novelty…

27.Machine Learning: Tell Truths from Data Recommender Systems: Recommend Truths from Data Data with Machine Learning Methods Data with Recommender Systems • Attributes • User/Item features • Regression • Clustering • Factor Analysis • Labels • Ratings • Classification • Learning to Rank • Images, Videos • Item pictures • Computer Vision Approach

28.Machine Learning: Tell Truths from Data (Cont.) Recommender Systems: Recommend Truths from Data Data with Machine Learning Methods Data with Recommender Systems • Text • Reviews • Natural Language Processing (NLP) • Sentiment Analysis • Sequence • Transaction • Time Series Analysis • Network • Link Prediction, Network Embedding • User/Item Network

29.In a word • Data is the matchmaker to bring machine learning to recommender systems