Stepan Balcar, Peter Vojtas and Michal Kopecky Charles University - PowerPoint PPT Presentation

Stepan Balcar, Peter Vojtas and Michal Kopecky Charles University e-mail(s): <name>.<surname>@mff.cuni.cz

Outline — Introduction — Nature-inspired algorithms for matrix factorization — Island models — Results — Future Work DA2PL 2018 – Balcar, Vojtas , Kopecky Synergy of stochastic and … in recommender systems 2

Recommendation The problem Illustration — Recommendation of items to users based on their preferences — We have chosen domain of movies Databse of items Input: and preferences Result: — MovieLens dataset s — ML-100k 100000 ratings by 943 users on 1682 movies — ML-1M 1000209 ratings, by 6040 users on User + 3900 movies profile Recommended — Usable also (and not only) for items Recommendation recommendation of — Music, books, recipes, holidays DA2PL 2018 – Balcar, Vojtas , Kopecky Synergy of stochastic and … in recommender systems 3

Our approach Our model Matrix factorization — Collaborative filtering — If possible, content-based filtering — Based on matrix factorization — Extraction of latent vectors of users and items — Model: — Pair of latent vectors — Nature inspired computation — Genetic algorithms — Neural networks? DA2PL 2018 – Balcar, Vojtas , Kopecky Synergy of stochastic and … in recommender systems 4

Evolutionary algorithms and SGD — Individual = pair of latent vectors — Operators based on SGD — Example: - train set → count SGD-gradients — Cross = One-point cross - train set → evluation — Mutation = SGD DA2PL 2018 – Balcar, Vojtas , Kopecky Synergy of stochastic and … in recommender systems 5

Distributed computing — More approaches — Master-slave model — Cellular model — Island model DA2PL 2018 – Balcar, Vojtas , Kopecky Synergy of stochastic and … in recommender systems 6

Co-evolution and Migration — Individuals from one island can time to time migrate to another island — Mixing of “genetic material” — Dynamic re/planning — Island can change parameters, or even the used method — Stochastic: Hill climbing, Random search, Simulated annealing, Tabu search, — Evolutionary: Brute force, Differential evolution — Based on external planner — Possibility to take advantage of different approaches to learning to gain advantage — Mutual help of methods — SYNERGY DA2PL 2018 – Balcar, Vojtas , Kopecky Synergy of stochastic and … in recommender systems 7

Experiments DA2PL 2018 – Balcar, Vojtas , Kopecky Synergy of stochastic and … in recommender systems 8

Results — si-* — Single method — par-* — Parallel approach — coe-* — Co-evolution of more methods DA2PL 2018 – Balcar, Vojtas , Kopecky Synergy of stochastic and … in recommender systems 9

Preliminary NN results — At this moment, NN approach seems to outperform our results — NN available (https://github.com/zishansami102/Recommendation- Engine) and their modifications achieve RMSE under 0,85 – better than 0,88 — Our preliminary tests of the NN behavior show that around 20 th epoch the best results were achieved. Some settings achieved RMSE around 0.835. — Further training shows overfitting. — While the RMSE on training data continues its descent, — the RMSE on testing data starts to grow back to the 0.85 level. — Varying top-k (originally 5) considered ratings per user does not substantially change the picture. The optimum is moved to higher epochs. — Looks promising, — The lost of generalization capability on ml-1m dataset — might mean the ability to learn user preferences on bigger datasets DA2PL 2018 – Balcar, Vojtas , Kopecky Synergy of stochastic and … in recommender systems 10

Future work — Utilize the maximum potential of cooperation between different methods — Ensemble — Meta/hyper heuristics — Create a dynamic adaptive portfolio for online recommendations — Use of existing optimized library implementations of methods whenever possible for speedup of computation — Involving NN somehow in the island model — Introduce “ Deep School Island ”, that — will prepare promising individuals for other islands/algorithms — ? will adopt promising individuals from outside and try to optimize them further ? — … 11

Previous related publications — Concerned on recommenders — Balcar Š.: Influence of the individual's size on the island model architecture, in Digital Library University of West Bohemia, Brno, University of West Bohemia, ISBN: 978-80-214-5679-2, pp. 163-168, 2018 — Balcar Š.: Preference learning by matrix factorization on island models, in Proceedings of the 18th Conference Information Technologies - Applications and Theory (ITAT 2018), Krompachy, CEUR Workshop Proceedings, ISBN: 978-1-72726-719-8, ISSN: 1613-0073, pp. 146-151, 2018 — Concerned on combinatorial optimization (TSP, ...) — Balcar Š., Pilát M.: Heterogeneous Island Model with Re-planning of Methods, in GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, Kyoto, Association for Computing Machinery, Inc, ISBN: 978-1-4503-5764-7, pp. 245-246, 2018 — Balcar Š., Pilát M.: Online Parallel Portfolio Selection with Heterogeneous Island Model, in International Conference on Tools with Artificial Intelligence, Volos, Greece, 2018 12

Questions? ITAT 2017 - Kopecky, Vomlelova, Vojtas Repeatable Web Data Extraction and Interlinking 13

Implementation of island models: https://github.com/sbalcar/distributedea/ 14