A Hybrid Conditional Variational Autoencoder Model for Personalised Top-n Recommendation

The interactions of users with a recommendation system are in general sparse, leading to the well-known cold-start problem. Side information, such as age, occupation, genre and category, have been widely used to learn latent representations for users and items in order to address the sparsity of users' interactions. Conditional Variational Autoencoders (CVAEs) have recently been adapted for integrating side information as conditions to constrain the learned latent factors and to thereby generate personalised recommendations. However, the learning of effective latent representations that encapsulate both user (e.g. demographic information) and item side information (e.g. item categories) is still challenging. In this paper, we propose a new recommendation model, called Hybrid Conditional Variational Autoencoder (HCVAE) model, for personalised top-n recommendation, which effectively integrates both user and item side information to tackle the cold-start problem. Two CVAE-based methods -- using conditions on the learned latent factors, or conditions on the encoders and decoders -- are compared for integrating side information as conditions. Our HCVAE model leverages user and item side information as part of the optimisation objective to help the model construct more expressive latent representations and to better capture attributes of the users and items (such as demographic, category preferences) within the personalised item probability distributions. Thorough and extensive experiments conducted on both the MovieLens and Ta-feng datasets demonstrate that the HCVAE model conditioned on user category preferences with conditions on the learned latent factors can significantly outperform common existing top-n recommendation approaches such as MF-based and VAE/CVAE-based models

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Group Learning and Opinion Diffusion in a Broadcast Network

We analyze the following group learning problem in the context of opinion diffusion: Consider a network with $M$ users, each facing $N$ options. In a discrete time setting, at each time step, each user chooses $K$ out of the $N$ options, and receive randomly generated rewards, whose statistics depend on the options chosen as well as the user itself, and are unknown to the users. Each user aims to maximize their expected total rewards over a certain time horizon through an online learning process, i.e., a sequence of exploration (sampling the return of each option) and exploitation (selecting empirically good options) steps. Within this context we consider two group learning scenarios, (1) users with uniform preferences and (2) users with diverse preferences, and examine how a user should construct its learning process to best extract information from other's decisions and experiences so as to maximize its own reward. Performance is measured in {\em weak regret}, the difference between the user's total reward and the reward from a user-specific best single-action policy (i.e., always selecting the set of options generating the highest mean rewards for this user). Within each scenario we also consider two cases: (i) when users exchange full information, meaning they share the actual rewards they obtained from their choices, and (ii) when users exchange limited information, e.g., only their choices but not rewards obtained from these choices