TJTS505: Master's Thesis Seminar Lecture 3: Make an a4 plan Dr. Shuaiqiang Wang Department of CS-IS, JYU

A4 plan A4-plan Concepts correspond to the plan. Refine and customize your topic. Discuss with your supervisor

A4 Plan The author's name. Topic of the thesis. Supervisor (s). A brief description of the subject, and definitions of the key terms. Motivation (Why do you want to investigate it?). Preliminary research questions. The research method, if it can already draw. A few scientific sources. Capacity to carry out the work.

Example Shuaiqiang Wang, Shanshan Huang, Tie-Yan Liu, Jun Ma, Zhumin Chen, Jari Veijalainen. Ranking-oriented Collaborative Filtering: A Listwise Approach. ACM Transactions on Information Systems (TOIS), 35(2): No.10, 2016. (JUFO-3)

subject and key terms Subject Recommender system: In recent years, recommender system has been widely and successfully applied in many online websites such as eBay, Amazon and Netflix Collaborative filtering: Collaborative filtering (CF) is one of the most successful recommendation techniques to build recommender systems. CF is based on the assumption that users will rate items similarly or have similar behaviors in the future if they rated or acted similarly in the past [1] Ranking-oriented CF: Traditional rating-oriented CF might not be able to achieve the ultimate goal of recommender systems: presenting a ranking or recommendation list to a user rather than predict the absolute value of the ratings

Key terms Collaborative filtering, Ranking-oriented collaborative filtering, Recommender systems

Motivation Ranking-oriented CF can be memory-based or model- based. We focus on memory-based algorithms, since they have demonstrated many advantages such as strong robustness, interpretability, and competitive performance [2,3] Existing algorithms can be regarded as pairwise algorithms: High computational complexity and might loss accuracy [2,3] Listwise algorithms [6] might be helpful!

research questions RQ1: How to design a reasonable listwise algorithm with lower computational complexity and higher accuracy? RQ2: What is the relationship between listwise algorithms and other CF paradigms (pairwise, rating-oriented)? RQ3: Is our proposed algorithm more effective than state- of-the-art algorithms? RQ4: What is the impact of the parameters to the proposed algorithm? RQ5: Can we further improve the efficiency/accuracy of the proposed algorithm?

research method Follow the framework of the memory-based CF algorithms [1,2,3] Since the categorization of the CF algorithms is very similar to that of learning to rank algorithms, the formulation of listwise learning to rank algorithms [6] could be helpful. Benchmark datasets: MovieLens, EachMovie, Netflix Baselines: Rating-oriented CF [1], EigenRank [2], VSRank [3], CofiRank [4], ListRank-MF [6

Key literatures J. S. Breese et al. 1998. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In UAI. 43–52. N. N. Liu and Q. Yang. 2008. Eigenrank: A Ranking-Oriented Approach to Collaborative Filtering. In SIGIR. 83–90. S. Wang et al. 2014. VSRank: A Novel Framework for Ranking-based Collaborative Filtering. ACM Transactions on Intelligent Systems and Technology 5, 3 (2014), No.51. M. Weimer et al. 2007. CofiRank - Maximum Margin Matrix Factorization for Collaborative Ranking. In NIPS. 1329–1336. Y. Shi et al. 2010. List-wise Learning to Rank with Matrix Factorization for Collaborative Filtering. In RecSys. 269–272. Z. Cao et al. 2007. Learning to Rank: From Pairwise Approach to Listwise Approach. In (ICML. 129–136.

homework Try to make your A4 research plan Email: gpandey_01@yahoo.com Subject of your email: TJTS505: A4 research plan + Your full name Deadline: 6PM, October 20 Demonstrate your A4 plan Oral presentation with slides Time: October 21

Any Questions?