Learning users' interests by quality classification in market-based recommender systems

Recommender systems are widely used to cope with the problem of information overload and, to date, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents who supplied them according to the users’ ratings of their suggestions. Moreover, we have theoretically shown how our system incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively in practice, however, each agent needs to be able to classify its recommendations into different internal quality levels, learn the users’ interests for these different levels, and then adapt its bidding behaviour for the various levels accordingly. To this end, in this paper we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy does indeed help the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations

Deep recommender engine based on efficient product embeddings neural pipeline

Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the "holy grails" of predictive analytics is the research and development of the "perfect" recommendation system. In our paper, we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems. Our highly parallelized hybrid model pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively. Our experimentation and benchmarking processes have been done using pharma industry retail real-life transactional Big-Data streams.Comment: 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet