Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

pdf/a-survey-on-conversational-recommender-systems-2kntb7i9rr.pdf

A Survey on Conversational Recommender Systems

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this article, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

In this article we argue and give evidence that the research on group recommender systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be truly beneficial for groups. We illustrate the adopted research method and the results of a user study aimed at observing and measuring the evolution of user preferences and interaction in a tourism decision-making task: finding a destination to visit together as a group. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings have on the design of interactive group recommender systems.

/pdf/an-observational-user-study-for-group-recommender-systems-in-3nnzn6pamo.pdf

An observational user study for group recommender systems in the tourism domain

Collaborative filtering is widely used in recommendation systems. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e., provide sufficient ratings. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a tradeoff between the rating cost and the recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated ratings are used for generating recommendations, we design a behavior rule which allows users to achieve an SE via iteratively rating items. We theoretically analyze under what conditions an SE can be learned via the behavior rule. Experimental results on Jester and MovieLens data sets confirm the analysis and demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.

User Participation in Collaborative Filtering-Based Recommendation Systems: A Game Theoretic Approach

Group recommender systems (GRSs) support users in a group when facing decision making tasks Many GRSs simply perform a one shot aggregation of the individual preferences and then compute recommendations In this paper, we propose a different approach; it allows users to iteratively express and revise their preferences during the decision making process and supports such a conversational process with a chat-based interface The implemented system observes users' interactions with the discussed options and offers appropriate support as well as recommendations to facilitate group members to come up with an agreement and a final decision We have evaluated the effectiveness of our approach by conducting a controlled live user study The empirical results show that the mobile application that implements the proposed approach achieves usability as well as high recommendation quality and choice satisfaction

Dynamic elicitation of user preferences in a chat-based group recommender system

A recommender system (RS) can infer constraints on the user utility function by observing the queries selected by a user among those it has suggested. Reasoning on these constraints it can avoid suggesting queries that retrieve products with an inferior utility, i.e., dominated queries. In this paper we propose a new efficient technique for the computation of dominated queries. It relies on the system's assumption that the number of possible profiles (utility functions) of the users it may interact with is finite. Under this assumption query suggestions can be efficiently computed and their number can be kept small. Moreover, we show that even if the system is not contemplating all the possible user profiles its performance is very close to the optimal one.

/pdf/inferring-user-utility-for-query-revision-recommendation-55yoztqbrb.pdf

Inferring user utility for query revision recommendation

Query revisions in a conversational system can be efficiently computed by assuming that the profiles of the potential users are in a predefined, a priori known and finite set. However, without any additional knowledge of the actual profiles distribution, the system may miss the true profiles of the users, hence deteriorating the system performance. We propose a method for identifying a tailored set of profiles that is acquired by analysing the implicitly shown preferences of the users that interacted with the system. We show that with the proposed method the system can efficiently identify good query revisions.

https://www.inf.unibz.it/~ricci//papers/sys153-blanco.pdf

Acquiring user profiles from implicit feedback in a conversational recommender system

Observing the queries selected by a user, among those suggested by a recommender system, one can infer constraints on the user’s utility function, and can avoid suggesting queries that retrieve products with an inferior utility, i.e., dominated queries. In this paper we propose a new e ffi cient technique for the computation of dominated queries. It relies on the system’s assumption that the number of possible profiles (or utility functions), of the users it may interact with, is finite. We show that making query suggestions is simplified, and the number of suggestions is strongly reduced. We also found that even if the system is not contemplating the true user profile, among the above mentioned finite set of profiles, its performance is still very close to the optimal one.

/pdf/recommending-personalized-query-revisions-sgki8oor5i.pdf

Recommending Personalized Query Revisions

Information Recommendation is a conversational approach aimed at suggesting to the user how to reformulate his queries to a product catalogue in order to find the products that maximize his utility. In previous work, it was shown that, by observing the queries selected by the user among those suggested, the system can make inferences on the true user utility function and eliminate from the set of suggested queries those retrieving products with an inferior utility (dominated queries). The computation of the dominated queries was based on the solution of several linear programming problems, which represented a major computational bottleneck for the efficiency of the proposed solution. In this paper we propose a new technique for the computation of the dominated queries. It relies on the assumption that the set of possible user utility functions is finite. We show that under this assumption the computation of the query suggestions is simplified and the number of query suggestions is strongly reduced.

/pdf/conversational-query-revision-with-a-finite-user-profiles-3ten41jgra.pdf

Conversational Query Revision with a Finite User Profiles Model.

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Inferring user utility for query revision recommendation

Acquiring user profiles from implicit feedback in a conversational recommender system

Recommending Personalized Query Revisions

Conversational Query Revision with a Finite User Profiles Model.