Leveraging DataAnalysis Session Logs for Efficient, Personalized, Interactive View Recommendation View recommendation has been recently adopted to assist data analysts in better understanding the data. In order to recommend useful views, existing view recommendation approaches propose a variety of utility functions, each suitable for a different usage scenario. However, as the interestingness of a recommended view is userdependent, no single utility function can represent usersu0027 preferences and intentions in all cases. With the richly available choices for utility functions, identifying the most appropriate ones along with their tunable parameters remains a challenge even for expert users. To help identify the most appropriate utility function, existing works have made attempts in two different directions, 1) providing generic recommendations of utility functions by learning offline from historical logs, and 2) providing personalized recommendations of utility functions by learning from the interactions with each particular user. Both proposed approaches exhibit clear advantages and disadvantages. In this work, to benefit from both approaches, we device a novel hybrid interactive view recommendation solution, namely HolisticViewSeeker (HVS), that effectively combines the offline learning with the online interactive learning to provide personalized view recommendation. Our experimental evaluations conducted on realworld data show that HVS outperforms both stateoftheart online and offline approaches by a significant margin in multiple respects.

ICARS: An Interactive ContextAware Recommender System Contextaware recommendation has attracted significant attentions over online sites due to its smart context adaption in improving recommendation quality. However, the useru0027s instant contexts do not follow hisher regular user behaviour patterns, thus have not been well captured for advanced personalization of recommendation generation. In this work, we propose an Interactive ContextAware Recommender System (ICARS), which allows users to interact and present their needs, so the system can personalize and refine user preferences. ICARS iteratively asks a question to a user to trigger feedback in term of her recent contexts and incorporates the response to recommend items most likely satisfying hisher instant interests. Specifically, we first propose a Personalized Weighted ContextAware Matrix Factorization (PWCAMF) that enables the personalization of important contexts for each user. Then we propose two question selection strategies that exploit user preferences through feedback. We have conducted comprehensive experiments over two real datasets. The experimental results prove the effectiveness of our ICARS system compare to existing competitors.