PIPE: Personalizing Recommendations via Partial Evaluation
<|reference_start|>PIPE: Personalizing Recommendations via Partial Evaluation: It is shown that personalization of web content can be advantageously viewed as a form of partial evaluation --- a technique well known in the programming languages community. The basic idea is to model a recommendation space as a program, then partially evaluate this program with respect to user preferences (and features) to obtain specialized content. This technique supports both content-based and collaborative approaches, and is applicable to a range of applications that require automatic information integration from multiple web sources. The effectiveness of this methodology is illustrated by two example applications --- (i) personalizing content for visitors to the Blacksburg Electronic Village (http://www.bev.net), and (ii) locating and selecting scientific software on the Internet. The scalability of this technique is demonstrated by its ability to interface with online web ontologies that index thousands of web pages.<|reference_end|>
@article{ramakrishnan1999pipe:,
    title={PIPE: Personalizing Recommendations via Partial Evaluation},
    author={Naren Ramakrishnan},
    journal={arXiv preprint arXiv:cs/9910015},
    year={1999},
    archivePrefix={arXiv},
    eprint={cs/9910015},
    primaryClass={cs.IR cs.AI}
}