A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

A personalized system for conversational recommendations

technical reportIncreased computing power and theWeb have made information widely accessible. In turn, this has encouraged the development of recommendation systems that help users find items of interest, such as books or restaurants. Such systems are more useful when they personalize themselves to each user?s preferences, thus making the recommendation process more efficient and effective. In this paper, we present a new type of recommendation system that carries out a personalized dialogue with the user. This system ? the Adaptive Place Advisor ? treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. The system incorporates a user model that contains item, attribute, and value preferences, which it updates during each conversation and maintains across sessions. The Place Advisor uses both the conversational context and the user model to retrieve candidate items from a case base. The system then continues to ask questions, using personalized heuristics to select which attribute to ask about next, presenting complete items to the user only when a few remain. We report experimental results demonstrating the effectiveness of user modeling in reducing the time and number of interactions required to find a satisfactory item

The Effect of Personalization Techniques in Users’ Perceptions of Conversational Recommender Systems

Conversational recommender systems provide users with individually tailored recommendations in a flowing dialogue. These require users to disclose information proactively or reactively for receiving personalized recommendations, which can trigger users' resistance to the platform and to the recommendations. Accordingly, this study examined the extent to which user-initiated and system-initiated recommendations provided by a conversational recommender system influenced users' perceptions of it. The results of an online experiment entail that when recommendations are system-initiated, as compared to user-initiated, users perceive to be in less control and perceive the system as riskier. Furthermore, the results stress that systems that provide user-initiated or system-initiated recommendations do not differ in users' perceptions of anthropomorphism

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

Sense - Recommendation System based on Affective Interpretations of Social Media Posts: A Proposed User Interface Design

It is now easier and cheaper for people to get involved in technology, and most people are obtaining or receiving tons of information nowadays, such as recommendations. Different recommendations are filled with our devices like smartphones, tablets or even wearable devices. However, most searches and recommendation systems fail to take into consideration the emotional context related to a user’s input and action, and as a consequence, people can hardly receive personalized, emotion-related, user-based recommendations. Though people are given various choices about lives based on searches and saved data, those recommendations can hardly improve the stress and difficulties of accomplishing daily life goals caused by the developing of technology. Therefore, People need more user-centered recommendations that incorporate emotion and action related evaluations to reach more precise results. This thesis project presents a possible design solution to improve this situation. An affective recommendation system designed to determine a person’s emotional state or condition based on the affective interpretations of their social media content. Combining bio information, exercise or activity records, it provides personalized recommendations like food, entertainment, activities or exercise suggestions related to the users. This project demonstrates how a new user-centered, emotion and activity based recommendation system can leverage elements of emerging technologies such as conversational User Interfaces (CUI), context recognition, and expression recognition to create a more user-friendly and more meaningful experience

The benefits of opening recommendation to human interaction

This paper describes work in progress that uses an interactive recommendation process to construct new objects which are tailored to user preferences. The novelty in our work is moving from the recommendation of static objects like consumer goods, movies or books, towards dynamically-constructed recommendations which are built as part of the recommendation process. As a proof-of-concept we build running or jogging routes for visitors to a city, recommending routes to users according to their preferences and we present details of this system