An investigation on the impact of natural language on conversational recommendations

In this paper, we investigate the combination of Virtual Assistants and Conversational Recommender Systems (CoRSs) by designing and implementing a framework named ConveRSE, for building chatbots that can recommend items from different domains and interact with the user through natural language. An user experiment was carried out to understand how natural language influences both the cost of interaction and recommendation accuracy of a CoRS. Experimental results show that natural language can indeed improve user experience, but some critical aspects of the interaction should be mitigated appropriately

Evaluating Conversational Recommender Systems: A Landscape of Research

Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way. With the latest advances in voice-controlled devices, natural language processing, and AI in general, such systems received increased attention in recent years. Technically, conversational recommenders are usually complex multi-component applications and often consist of multiple machine learning models and a natural language user interface. Evaluating such a complex system in a holistic way can therefore be challenging, as it requires (i) the assessment of the quality of the different learning components, and (ii) the quality perception of the system as a whole by users. Thus, a mixed methods approach is often required, which may combine objective (computational) and subjective (perception-oriented) evaluation techniques. In this paper, we review common evaluation approaches for conversational recommender systems, identify possible limitations, and outline future directions towards more holistic evaluation practices

A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations

One potential solution to help people change their eating behavior is to develop conversational systems able to recommend healthy recipes. Beyond the intrinsic quality of the recommendations themselves, various factors might also influence users? perception of a recommendation. Two of these factors are the conversational skills of the system and users' interaction modality. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users? eating habits and current preferences. Users can interact with Cora in two different ways. They can select predefined answers by clicking on buttons to talk to Cora or write text in natural language. On the other hand, Cora can engage users through a social dialogue, or go straight to the point. We conduct an experiment to evaluate the impact of Cora's conversational skills and users' interaction mode on users' perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users' perception of the interaction as well as their perception of the system

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Social contextuality and conversational recommender systems

As people continue to become more involved in both creating and consuming information, new interactive methods of retrieval are being developed. In this thesis we examine conversational approaches to recommendation, that is, the act of suggesting items to users based on the system’s understanding of them. Conversational recommendation is a recent contribution to the task of information discovery. We propose a novel approach to conversation around recommendation, examining how it is improved to work with collaborative filtering, a common recommendation algorithm. In developing new ways to recommend information to people we also examine their methods of information seeking, exploring the role of conversational recommendation, using both interview and sensed brain signals. We also look at the implications of the wealth of social and sensed information now available and how it improves the task of accurate recommendation. By allowing systems to better understand the connections between users and how their social impact can be tracked we show improved recommendation accuracy. We look at the social information around recommendations, proposing a directed influence approach between socially connected individuals, for the purpose of weighting recommendations with the wisdom of influencers. We then look at the semantic relationships that might seem to indicate wisdom (i.e. authors on a book-ranking site) to see if the ``wisdom of the few'' can be traced back to those conventionally considered wise in the area. Finally we look at ``contextuality'' (the ability of sets of contextual sensors to accurately recommend items across groups of people) in recommendation, showing that different users have very different uses for context within recommendation. This thesis shows that conversational recommendation can be generalised to work well with collaborative filtering, that social influence contributes to recommendation accuracy, and that contextual factors should not be treated the same for each user