PIPE: Personalizing Recommendations via Partial Evaluation

TL;DR

This paper introduces a novel personalization method for web content using partial evaluation, modeling recommendation as program specialization based on user preferences, applicable to diverse web applications.

Contribution

It presents a new approach to web personalization by applying partial evaluation, bridging programming techniques with recommendation systems.

Findings

Effective personalization demonstrated on electronic village and scientific software sites.

Supports content-based and collaborative recommendation approaches.

Scalable to web ontologies indexing thousands of pages.

Abstract

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.