Abstract
PeopleViews is a Human Computation based environment for the construction of constraint-based recommenders. Constraint-based recommender systems support the handling of complex items where constraints (e.g., between user requirements and item properties) can be taken into account. When applying such systems, users are articulating their requirements and the recommender identifies solutions on the basis of the constraints in a recommendation knowledge base. In this paper, we provide an overview of the PeopleViews environment and show how recommendation knowledge can be collected from users of the environment on the basis of micro-tasks. We also show how PeopleViews exploits this knowledge for automatically generating recommendation knowledge bases. In this context, we compare the prediction quality of the recommendation approaches integrated in PeopleViews using a DSLR camera dataset.
Highlights
IntroductionIn contrast to collaborative filtering (Goldberg et al 1992; Konstan et al 1997), contentbased filtering (Pazzani and Billsus 1997; Roy and Mooney 2004), and case-based recommendation (Burke and Hammond 1997; McCarthy et al 2005; Musto et al 2014), constraint-based recommender systems (as a specific type of knowledge-based recommender system) (Burke 2000; Felfernig and Burke 2008; Jannach et al 2010) rely on a predefined set of constraints that perform the selection of candidate items (Burke and Ramezani 2010)
The potential advantages of applying PEOPLEVIEWS technologies are less efforts related to recommendation knowledge base development and maintenance, fewer erroneous constraints, and a significantly higher degree of scalability, i.e., more recommenders can be maintained in parallel
In order to evaluate the different recommendation approaches currently integrated in the system, we used a PEOPLEVIEWS dataset created by users with expertise in the domain of digital cameras
Summary
In contrast to collaborative filtering (Goldberg et al 1992; Konstan et al 1997), contentbased filtering (Pazzani and Billsus 1997; Roy and Mooney 2004), and case-based recommendation (Burke and Hammond 1997; McCarthy et al 2005; Musto et al 2014), constraint-based recommender systems (as a specific type of knowledge-based recommender system) (Burke 2000; Felfernig and Burke 2008; Jannach et al 2010) rely on a predefined set of constraints that perform the selection of candidate items (Burke and Ramezani 2010). For complex items, content-based and collaborative filtering approaches can not be directly applied, since these approaches do not allow the inclusion of rules and/or constraints Such an inclusion of constraints comes along with a major challenge which is the knowledge acquisition bottleneck: domain experts and knowledge engineers have to extensively communicate to correctly encode the recommendation knowledge. The potential advantages of applying PEOPLEVIEWS technologies are less efforts related to recommendation knowledge base development and maintenance, fewer erroneous constraints (compared to knowledge engineers, domain experts know more about the item domain), and a significantly higher degree of scalability, i.e., more recommenders can be maintained in parallel (which could not be achieved by a small number of knowledge engineers). We introduce a new approach to the development of knowledge bases for constraint-based recommendation scenarios This approach helps to assure scalability since it makes it possible to engage domain experts without a computer science background into knowledge engineering tasks.
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