Evaluating collaborative filtering over time

• Main Author: Lathia, N. K.
• Published: University College London (University of London) 2010
• Subjects: 004
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.565046

Recommender systems have become essential tools for users to navigate the plethora of content in the online world. Collaborative filtering—a broad term referring to the use of a variety, or combination, of machine learning algorithms operating on user ratings—lies at the heart of recommender systems’ success. These algorithms have been traditionally studied from the point of view of how well they can predict users’ ratings and how precisely they rank content; state of the art approaches are continuously improved in these respects. However, a rift has grown between how filtering algorithms are investigated and how they will operate when deployed in real systems. Deployed systems will continuously be queried for personalised recommendations; in practice, this implies that system administrators will iteratively retrain their algorithms in order to include the latest ratings. Collaborative filtering research does not take this into account: algorithms are improved and compared to each other from a static viewpoint, while they will be ultimately deployed in a dynamic setting. Given this scenario, two new problems emerge: current filtering algorithms are neither (a) designed nor (b) evaluated as algorithms that must account for time. This thesis addresses the divergence between research and practice by examining how collaborative filtering algorithms behave over time. Our contributions include: 1. A fine grained analysis of temporal changes in rating data and user/item similarity graphs that clearly demonstrates how recommender system data is dynamic and constantly changing. 2. A novel methodology and time-based metrics for evaluating collaborative filtering over time, both in terms of accuracy and the diversity of top-N recommendations. 3. A set of hybrid algorithms that improve collaborative filtering in a range of different scenarios. These include temporal-switching algorithms that aim to promote either accuracy or diversity; parameter update methods to improve temporal accuracy; and re-ranking a subset of users’ recommendations in order to increase diversity. 4. A set of temporal monitors that secure collaborative filtering from a wide range of different temporal attacks by flagging anomalous rating patterns. We have implemented and extensively evaluated the above using large-scale sets of user ratings; we further discuss how this novel methodology provides insight into dimensions of recommender systems that were previously unexplored. We conclude that investigating collaborative filtering from a temporal perspective is not only more suitable to the context in which recommender systems are deployed, but also opens a number of future research opportunities.