Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users' behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions.

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Abstract. Recommender systems are intelligent applications that assist the users in a decision-making process by giving personalized product recommendations. Quite recently conversational approaches have been introduced to support a more interactive recommendation process. Notwithstanding the increased interactivity offered by these approaches, the system activity is rigid and follows an execution path that must be defined apriori, at design time. In this paper, we present a new type of recommender system capable of learning an interaction strategy by observing the effects of its actions on the user and on the final outcome of the recommendation session. We view the recommendation process as a sequential decision problem, and we model it as a Markov Decision Process. We evaluate this approach in a case study where the goal is to learn the best support strategy for advising a user in refining a query to a product catalogue. The experimental results demonstrate the value of our approach and show that an initial fixed strategy can be improved by first learning a model of the user behavior and then applying the Policy Iteration algorithm to compute the optimal recommendation policy.

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

Conversational recommender systems support a structured human-computer interaction in order to assist online tourists in important online activities such as travel planning. In this article we describe the effects and advantages of a novel recommendation methodology based on Machine Learning techniques that allows conversational systems to autonomously improve an initial strategy in order to learn a new one that is more effective and efficient. We applied and tested our approach within a prototype of an online travel recommender system in collaboration with the Austrian Tourism portal (Austria.info). In this article, we present the features of this technology and the results of the online evaluation. We show that the learned strategy adapts its actions to the served users and deviates from a rigid initial strategy. More importantly, we show that the optimal strategy is able to assist online tourists in acquiring their goals more efficiently than the initial strategy. It can be used by the system designer to understand the limitations of an existing interaction design and guide him in the adoption of a new one that is able to improve customer relationship, the usage of their website, and the conversion rate of their online users.