Enhancing Personalization via Search Activity Attribution - Singla, Adish; White, Ryen W.; Awadallah, Ahmed; Horvitz, Eric (The 36th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014), 2014)
Online services rely on machine identifiers to tailor services such as personalized search and advertising to individual users. The assumption made is that each identifier comprises the behavior of a single person. However, shared machine usage is common, and in these cases, the activities of multiple users may be generated under a single identifier, creating a potentially noisy signal for applications such as search personalization. We propose enhancing Web search personalization with methods that can disambiguate among different users of a machine, thus connecting the current query with the appropriate search history. Using logs containing both person and machine identifiers, and logs from a popular commercial search engine, we learn models that accurately assign observed search behaviors to each of different users. This information is then used to augment existing personalization methods that are currently based only on machine identifiers. We show that this new capability to infer users can be used to improve the performance of existing personalization methods. The early findings of our research are promising and have implications for search personalization.