Where will you comment next? Exploiting comments for personalized recommendations

Since the advent of Web 2.0, users have not only increasingly created content, but also contributed reactions to content in the form of comments. Comments are challenging to analyze due to their short lengths and informal style, meaning that any individual comment provides very little data to work with and is highly variable. However, comments capture innate and an explicit opinion of a user that makes it invaluable towards personalization. In this work, we explore the possibilities of exploiting comments towards the end of personalized recommendations. Over the course of this work, we investigate the particular domain of news recommendation and report our findings through use of different recommenders evaluated offline. Our contributions include an evaluation strategy that allows for simulation of recommenders offline, a simplistic hybrid filtering technique that exploits the advantages of its root recommenders and various findings related to news recommendation in general. We perform a preliminary study into investigating whether users maybe attributed by comments they make and find that they are indeed attributable if the right features are considered. Utilizing the property of authorship attribution through comments, we achieve user-user similarity that ultimately aids in delivering recommendations. We find that freshness is an important aspect in news recommendation and therefore for the design of our recommender we build upon the freshness aspect while also achieving personalization by exploiting content, user-user similarity and the user neighbourhood.

Subject

recommendation systems
information retrieval
machine learning

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http://resolver.tudelft.nl/uuid:dba67190-9f2f-479a-9409-20fdcf7e69ee

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Student theses

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master thesis

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4314506_Thesis_updated_final.pdf

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