Collaborative Filtering Bandits

Abstract

Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.

Cold-Start Developer Recommendation in Software Crowdsourcing: A Topic Sampling Approach

“…The traditional approaches on recommendation are collaborative filtering (CF) approaches [3], such as singular value decomposition (SVD) [4], where the user gets items based on other items with similar patterns. However, an inherent prerequisite of CF is to have historical user-item interactions.…”

“…Li et al modeled personalized recommendation of news articles as a contextual bandit problem [12], and solved it by using user-click feedback to maximize total user clicks. In [3], CF was combined with exploration-exploitation strategies for content recommendation. Instead of cold-start users, it is better at solving cold-start item problem.…”

Analytic approximate solutions to the chemically reactive solute transfer problem with partial slip in the flow of a viscous fluid over an exponentially stretching sheet with suction/blowing

“…Along with the analytical methods, some computational methods to solve nonlinear problems from engineering and computer sciences have been developed by Li et al [17][18][19][20][21][22][23][24], Guo et al [25], Korda et al [26].…”

Mining λ-Maximal Cliques from a Fuzzy Graph

“…In particular, it utilizes the data mining and machine learning [1,2] (e.g., supervised and unsupervised learning), decision and optimization problems [3] (e.g., linear and integer programming, dynamic programming), sequential decision making under uncertainty (e.g., Markov decision processes and recommender systems) [4,5], and networks (e.g., fuzzy graphs and network algorithms) [6,7]. In this work, we design a novel approach on topological structure mining of social networks for future computational sustainability.…”