Time-Sensitive Recommendation From Recurrent User Activities
By making personalized suggestions, a recommender system is playing a crucial role in improving the engagement of users in modern web-services. However, most recommendation algorithms do not explicitly take into account the temporal behavior and the recurrent activities of users. Two central but less explored questions are how to recommend the most desirable item at the right moment, and how to predict the next returning time of a user to a service. To address these questions, we propose a novel framework which connects self-exciting point processes and low-rank models to capture the recurrent temporal patterns in a large collection of user-item consumption pairs. We show that the parameters of the model can be estimated via a convex optimization, and furthermore, we develop an efficient algorithm that maintains O(1/ ) convergence rate, scales up to problems with millions of user-item pairs and hundreds of millions of temporal events. Compared to other state-of-the-arts in both synthetic and real datasets, our model achieves superb predictive performance in the two time-sensitive recommendation tasks. Finally, we point out that our formulation can incorporate other extra context information of users, such as profile, textual and spatial features.
Paper proposes a new method for time-sensitive recommendations based on user activities. It is different from existing methods because it address the problem of time-varying user preferences. The paper also addresses prediction of the next returning time of a user. Learning is performed using an efficient optimization algorithm proposed by authors. Experimental section shows results on one synthetic data set in order to show that learning is efficient on large scale data and on two real data sets of modest size.First, is a problem of time-varying user preferences really that spread. It would be great to analyze the real data sets from experiments in order to make this claim. If so, is it varying mostly because new popular songs come out? Or there some other pattern?What is the motivation for predicting next returning time for user? Could you please motivate it before you claim it as contribution of a paper? How could a web-company use this prediction and for what?I believe there are some simple baselines for predicting the next returning time that were not tested: 1) the last time delay 2) the most occurred time delay. Also, I think that predicting the actual returning time of the user is too challenging. Instead, I would focus on a more simpler task that is easier to predict. For example predicting is user will have short delay (1-5 days), medium delay (5-14 days), long delay (14-33 days) or very long delay (>33 days) before he uses our service again.Experimental section: It was hard for me to get a sense of how significant the performance lift is compared to SVD. Is it groundbreaking (order of magnitude) or modest? Would the user feel the difference? How would these recommendations compare to recommending popular items? In many cases it is a hard baseline to beat, especially if popular items are re-calculated every day.Small comments:1) Typo in last paragraph on page 3: "and is abel to capture", abel -> able2) In first paragraph of section 2 you start mentioning retweets and link creation in social networks without any references. The change of context was confusing.. best to rephrase.