PERSONALIZED RECOMMENDATIONS FOR UNIDENTIFIED USERS BASED ON WEB BROWSING CONTEXT

摘要

Methods and systems for recommending items to an unknown user of a website are disclosed. In one aspect, browsing activity of known users is analyzed to determine which items or categories of items are most popular in a given context (time, place, device, etc.). The known user browsing activity (clickstream data) is used to generate a multi-dimensional attribute matrix. Matrix factorization and clustering are used to generate affinity scores for items based on user context. These scores are used to generate item recommendations for an unknown user in a particular browsing context. In some embodiments, personalized item recommendations are updated based on interactions made between the unknown user and content of the website.