A Layered Conversational Recommender System

In this thesis a new approach to building product recommender systems is introduced. By using a customer-centric dialogue, the customers' preferences are elicited. These are the basis for inferring utility estimations about the desired technical properties of the products in question. Systems built this way can both operate autonomously, e.g., in an online store, and support a salesperson directly at the point-of-sale. The core of the approach is formed by a layered domain description that models customer stereotypes and needs, product attributes, the products themselves, and the causal interrelations between customer and product properties. Maintenance of the domain description, i.e., keeping the model up-to-date in face of frequent changes, is facilitated by the clear separation of concerns provided by the layered structure. In fact, the most frequently used class of updates can be handled in an entirely automated way if some constraints are satisfied. On a high level of abstraction, the system behavior is described by State Charts that are parameterized according to the domain description. Those parts of the system description where State Charts would be too imprecise are implemented by separate components realizing the required complex semantics. From the domain description, a Bayesian network is generated that forms the core of the inference engine of the recommender system. The network essentially controls the system-initiated dialogue flow and the recommendation process. Due to the characteristics of Bayesian networks, it is possible to respond to user-initiated dialogue steps in a natural way. Moreover, an explanation of the current recommendation can be generated without having to explicitly encode additional information in the modeling layer. Finally, a database structure and the SQL queries necessary to obtain recommendations can be inferred from the corresponding parts of the domain description. Instantiation of the system to a specific business domain is supported by a dedicated maintenance application that hides the complexities of the underlying algorithms. Thus, day-to-day system updates by non-technical domain experts, e.g., product managers, are facilitated. The developed concepts were implemented in cooperation with a local industry partner who intends to apply the recommender system in the field of mobile communications.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Universität Passau

Year

2012

Author dc:creator

• Radde, Sven

Contributors dc:contributor

• Freitag, Burkhard

• Recommender Systems
• Artificial Intelligence
• Bayesian Networks
• Relational Databases

Statement dc:rights

• Standardbedingung laut Einverständniserklärung

Repository record source_url

https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/190

OAI identifier oai:identifier

oai:kobv.de-opus4-uni-passau:190