Beyond Keyword Search: Representations and Models for Personalization
CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE
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We live in an era of information overload. From online news to online shopping to scholarly research, we are inundated with a torrent of information on a daily basis. With our limited time, money and attention, we often struggle to extract actionable knowledge from this deluge of data. A common approach for addressing this challenge is personalization where results are automatically filtered to match the tastes and preferences of individual users. While showing promise, modern systems and algorithms for personalization face their own set of challenges, both technical and social in nature. On the technical side, these include the well-documented cold start problem, redundant result sets and an inability to move beyond simple user interactions, such as keyword queries and star ratings. From a social standpoint studies have shown that most Americans have negative opinions of personalization, primarily due to privacy concerns. In this thesis, we address these challenges by introducing interactive concept coverage, a general framework for personalization that incentivizes diversity, and applies in both queryless settings as well as settings requiring complex and rich user interactions. This framework involves framing personalized recommendation as a probabilistic budgeted max-cover problem where each item to be recommended is defined to probabilistically cover one or more concepts. From user interaction, we learn weights on concepts and affinities for items, such that solving the resulting optimization problem results in personalized, diverse recommendations. Theoretical properties of our framework guarantee efficient, near-optimal solutions to our objective function, and no-regret learning of user preferences.
- Information Science
- Statistics and Probability