This paper presents a conversation strategy for interactive recommendations using a chatbot. Chatbots have recently been attracting attention for their use as a flexible user interface. To develop an effective chatbot, it is important to determine what kind of questions to ask, what information should be provided, and how to process a user's responses for a given task. In this paper, we target a chatbot that uses a graphical user interface (GUI) and focus on the task of recommending an item that suits a user's preference. We propose a conversation strategy where a chatbot combines questions about a user's preferences and recommendations while soliciting user's feedback to them. The balance between the questions and recommendations is controlled by changing the parameter values. In addition, we propose a simulation model to evaluate the performance of interactive recommendation under different parameter values. The simulation results with a prototype dataset are presented and discussed.

Socialware is multiagent systems for supporting social activities in the symbiotic society. In this article, we focused on supporting people with cognitive disabilities such as dementia, aphasia, higher cerebral dysfunction and aged people with cognitive decline. We propose a general architecture of the socialware for people with disabilities as multiagent systems which are composed of personal agents, communication agents, and community agents. Three experimental systems are introduced: networked reminiscence system, walk navigation system using photographs, and conversation support for people with aphasia.

Network representation of data is key to a variety of fields and their applications including trading and business. A major source of data that can be used to build insightful networks is the abundant amount of unstructured text data available through the web. The efforts to turn unstructured text data into a network have spawned different research endeavors, including the simplification of the process. This study presents the design and implementation of TraCER, a pipeline that turns unstructured text data into a graph, targeting the business networking domain. It describes the application of natural language processing techniques used to process the text, as well as the heuristics and learning algorithms that categorize the nodes and the links. The study also presents some simple yet efficient methods for the entity-linking and relation classification steps of the pipeline.

In recent years, companies have been seeking communication skills from their employees. Increasingly more companies have adopted group discussions during their recruitment process to evaluate the applicants’ communication skills. However, the opportunity to improve communication skills in group discussions is limited because of the lack of partners. To solve this issue as a long-term goal, the aim of this study is to build an autonomous robot that can participate in group discussions, so that its users can repeatedly practice with it. This robot, therefore, has to perform humanlike behaviors with which the users can interact. In this study, the focus was on the generation of two of these behaviors regarding the head of the robot. One is directing its attention to either of the following targets: the other participants or the materials placed on the table. The second is to determine the timings of the robot’s nods. These generation models are considered in three situations: when the robot is speaking, when the robot is listening, and when no participant including the robot is speaking. The research question is: whether these behaviors can be generated end-to-end from and only from the features of peer participants. This work is based on a data corpus containing 2.5 h of the discussion sessions of 10 four-person groups. Multimodal features, including the attention of other participants, voice prosody, head movements, and speech turns extracted from the corpus, were used to train support vector machine models for the generation of the two behaviors. The performances of the generation models of attentional focus were in an F-measure range between 0.4 and 0.6. The nodding model had an accuracy of approximately 0.65. Both experiments were conducted in the setting of leave-one-subject-out cross validation. To measure the perceived naturalness of the generated behaviors, a subject experiment was conducted. In the experiment, the proposed models were compared. They were based on a data-driven method with two baselines: (1) a simple statistical model based on behavior frequency and (2) raw experimental data. The evaluation was based on the observation of video clips, in which one of the subjects was replaced by a robot performing head movements in the above-mentioned three conditions. The experimental results showed that there was no significant difference from original human behaviors in the data corpus and proved the effectiveness of the proposed models.