Cobot: Real Time Multi User Conversational Search and Recommendations Saurav Sahay Anushree Venkatesh Ashwin Ram College of Computing College of Computing College of Computing Georgia Tech Georgia Tech Georgia Tech ssahay@cc.gatech.edu avenkatesh6@gatech.edu ashwin@cc.gatech.edu ABSTRACT Cobot is a new intelligent agent platform that connects users This paper introduces a novel Conversational Search and through real-time and off-line conversations about their health Recommendation system that involves finding relevant in- and medical issues. Intelligent web based information agents formation based on social interactions and feedback along (conversational/community bots) participate in each conver- with augmented agent based recommendations. People in sation providing highly-relevant real-time informational rec- social groups can provide solutions (answers to questions)[3], ommendations and connecting people with relevant conver- pointers to databases or other people (meta-knowledge)[3][6], sations and other community members. Cobot provides an validation and legitimation of ideas[3][4], can serve as mem- innovative approach to facilitate easier information access al- ory aids[7] and help with problem reformulation[3]. “Guided lowing users to exchange information through a natural lan- participation”[11] is a process in which people co-construct guage conversational approach. Conversational Search(CS) knowledge in concert with peers in their community[12]. In- is an interactive and collaborative information finding in- formation seeking is mostly a solitary activity on the web teraction. The participants in this interaction engage in today. Some recent work on collaborative search reports social conversations aided with an intelligent information several interesting findings and the potential of this technol- agent (Cobot) that provides contextually relevant factual, ogy for better information access.[5][2][1][9] web search and social search recommendations. Cobot aims We are building a system called Cobot1 to address some to help users make faster and more informed search and dis- of these challenges. Cobot introduces a conversational envi- covery. It also helps the agent learn about conversations ronment that provides social search through conversations with interactions and social feedback to make better recom- integrated with intelligent semantic meta-search from the mendations. Cobot leverages the social discovery process by web. Users want to simplify their experience when perform- integrating web information retrieval along with the social ing an information finding task. Conversational Search is interactions and recommendations. about letting users collaboratively search and find in natu- ral language, leaving the task of user intent comprehension on the system. The participating agent interacts with users Categories and Subject Descriptors proving recommendations that the users can accept, reject, H.5.0 [Information Systems Applications]: General; H.3.3 like, dislike or suggest. [Information Storage and Retrieval]: Information Search and Retrieval; I.2.7 [Artificial Intelligence]: Natural Lan- 2. SYSTEM DESCRIPTION guage Processing Cobot is an intelligent agent platform that connects users through real-time and offline conversations. Cobot lives General Terms in a community, has a limited understanding of domains Design, Human Factors, Algorithms through ontologies and brings relevant information to the users by participating in the conversations. Cobot’s ’conver- Keywords sation engine’ monitors user conversations with other users in the community and provides/receives recommendations Real time Collaborative Information Access, Social Search, (links and snippets) based on the conversation to the par- Contextual Collaborative Filtering, Conversational Search ticipants. Cobot’s ‘community engine’ models conversations to capture user-user and user-information interactions. 1. INTRODUCTION Design Goals 1. Near real time conversational agent 2. Personalized as well as generic recommendations Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are 3. Agent learns with interaction not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to 4. Uses a structured internal organization of content republish, to post on servers or to redistribute to lists, requires prior specific 1 permission and/or a fee. We use the term Cobot for Cobot system as well as Cobot Copyright 200X ACM X-XXXXX-XX-X/XX/XX ...$10.00. agent interchangeably 5. Dynamically connects conversations to the right set of 2.3 Aggregated Web Search people for participation Identifying relevant documents for a particular user’s need without extensive search, in conversational manner is the 6. Helps the user talk about health issues. (Real time key objective for precise search. The right search queries conversations) need to be figured out with situation assessment from the conversational snippets. It is not desirable to return dozens 7. In the real time conversational process, provides rec- or hundreds of remotely relevant results, even if some of ommendations. (‘who to talk to’, ‘what to look at’) them will be highly relevant. The aim is to retrieve succes- sive recommendations that try to address the search prob- In the following sections, we briefly describe the features lem precisely. Cobot uses different shallow semantic parsing of the Cobot system. techniques for operationalizing a user’s intent into computa- 2.1 Real time Conversational Search tional form, dispatching to multiple, heterogeneous services, gathering and integrating results, and presenting them back Cobot provides a conversational interface that combines to the user as a set of solutions to their request. semantic language understanding and real time collabora- tive environment for information retrieval with contextually 2.4 Real time matching of participants to con- relevant recommendations. Cobot helps people find infor- versations mation faster with the aim for finding useful responses for Communities are made up of users who are grouped by completing the search intent. The conversational interface different information needs into dynamic cohorts. These on- allows for much more interactivity than one-shot search style line communities, through effective sharing and collabora- interfaces, which aids usability and improves intent under- tion, increase the utility of systems and help solve individ- standing. For example, Cobot recommends links and snip- ual problems more effectively. Cobot allows for connecting pets from relevant articles on the internet. It also makes so- two or more individuals to an online conversation based on cial recommendations to connect contextually relevant users the topic and context of conversation, mutual interests, and to the conversation. what they want to talk about at that time. The system The approach we have taken to address CS problems is allows any individual to find/join that conversation. by developing dynamic data structures that model it. We call this structure the “Socio-Semantic Model” - these con- versation nets maintain in memory models of the conver- 2.5 Socio-Semantic Collaborative Filtering sation, participants, participants’ immediate social connec- Filtering and recommendation are crucial in collaborative tions, concepts, relationships and information flow. systems enabling users to navigate an ever-growing deluge of information more effectively. Cobot’s recommendation 2.2 Socio-Semantic Model engine delivers quality information delivered through filters The Socio-Semantic Conversation Model is a dynamic mem- achieved from semantic and contextual understanding of ory data structure based on principles of experience based text along with captured users’ interests. It uses various agent architecture.[10] It supports interleaved retrieval of personalization techniques such as collaborative filtering on information by applying different memory retrieval algo- conversations and other entities in context. Natural lan- rithms. The model maintains the user’s social graph, the guage processing techniques are used to enhance the content conversation graph with the extracted semantic net for the based recommendations.[8] conversation. Some essential properties of the model are as follows: 3. SYSTEM ARCHITECTURE Figure 2 depicts the high level architecture of the Cobot • The model is socially aware of the participant and system. The Conversational Agent uses different modules his social network’s availability (to aid with Cohort for conversation analysis, search and recommendation and Matching) maintains a short-term conversation memory for each con- versation. The socio-semantic model/net is analogous to • The model provides bi-directional recommendation and the agent’s long term memory model where it stores all pro- feedback. (Both agent and the participant can add rec- cessed information about users, conversations, activities and ommendations) content descriptors. • The model understands limited domain terminology and is able to find semantic relationships amongst con- 4. SYSTEM PROTOTYPE cepts extracted from conversations. Figure 3 shows one screenshot of the initial system proto- type which is work in progress. This prototype is designed • The model is aware of user’s profile (such as interests for health related searches by incorporating medical ontolo- and ratings) for the agent to be able to use that infor- gies. Users actively engage in conversations by multi-user mation. chat, rating or adding recommendations. The agent moni- tors the environment to build user interaction models and The Socio-Semantic Model aims to provide storage and to improve search relevance. memory based retrieval for dynamic representation, update and reuse of users’ knowledge and experiences. Figure 1 depicts the user-centric domain information modeling ap- 5. CONCLUSION proach to jointly model the information context from users’ This paper proposes a collaborative system for conversa- perspective. tional search and recommendations. We are hypothesizing Figure 1: Socio-Semantic Net Figure 2: System Architecture Figure 3: Prototype Interface that such a Conversational Search system is more usable international conference on Intelligent user interfaces, for information access as compared to a solitary web search pages 171–177. ACM New York, NY, USA, 2006. experience. We briefly describe the design goals and fea- [6] E. A. Fox, D. Hix, L. T. Nowell, D. J. Brueni, D. Rao, tures involved in construction of the Cobot system. Socio- W. C. Wake, and L. S. Heath. Users, user interfaces, and objects: Envision, a digital library. J. Am. Soc. Semantic Conversation Modeling using Experience-based Agency Inf. Sci., 44(8):480–491, 1993. is a unified approach for addressing Conversational Search [7] I. Karasavvidis. Distributed Cognition and problem. The dynamic and self configuring memory struc- Educational Practice. Journal of Interactive Learning tures and the semantic net details enable memory retrieval Research, pages 11–29, 2002. from the storage. Automatic Cohort Matching based on [8] P. Melville, R. Mooney, and R. Nagarajan. Conversations and User Profiles incorporate a methodology Content-boosted collaborative filtering for improved to dynamically pull users for conversations. Unlike users recommendations. In Proceedings of the National Conference on Artificial Intelligence, pages 187–192. themselves having to find relevant conversations, the con- Menlo Park, CA; Cambridge, MA; London; AAAI versations find the users using this approach. Press; MIT Press; 1999, 2002. [9] S. Paul and M. Morris. Cosense: enhancing sensemaking for collaborative web search. In 6. 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