Learning to Personalize Trending Image Search Suggestion

Trending search suggestion is leading a new paradigm of image search, where user’s exploratory search experience is facilitated with the automatic suggestion of trending queries. Existing image search engines, however, only provide general suggestions and hence cannot capture user’s personal interest. In this paper, we move one step forward to investigate personalized suggestion of trending image searches according to users’ search behaviors. To this end, we propose a learning-based framework including two novel components. The first component, i.e., trending-aware  weight regularized matrix factorization (TA-WRMF), is able to suggest personalized trending search queries by learning user preference from many users as well as auxiliary common searches. The second component associates the most representative and trending image with each suggested query. The personalized suggestion of image search consists of a trending textual query and its associated trending image. The combined textual-visual queries not only are trending (bursty) and personalized to user’s search preference, but also provide the compelling visual aspect of these queries. We evaluate our proposed learning-based framework on a large-scale search logs with 21 million users and 41 million queries in two weeks from a commercial image search engine. The evaluations demonstrate that our system achieve about 50% gain compared with state-of-the-art in terms of query prediction accuracy.