Online interactive recommender systems strive to promptly suggest to consumers appropriate items (e.g., movies, news articles) according to the current context including both the consumer and item content information. However, such context information is o en unavailable in practice for the recommendation, where only the users' interaction data on items can be utilized. Moreover, the lack of interaction records, especially for new users and items, worsens the performance of recommendation further. To address these issues, collaborative ltering (CF), one of the recommendation techniques relying on the interaction data only, as well as the online multi-armed bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted in the online interactive recommendation se ings, by assuming independent items (i.e., arms). Nonetheless, the assumption rarely holds in reality, since the real-world items tend to be correlated with each other (e.g., two articles with similar topics).In this paper, we study online interactive collaborative ltering problems by considering the dependencies among items. We explicitly formulate the item dependencies as the clusters on arms, where the arms within a single cluster share the similar latent topics. In light of the topic modeling techniques, we come up with a generative model to generate the items from their underlying topics. Furthermore, an e cient online algorithm based on particle learning is developed for inferring both latent parameters and states of our model. Additionally, our inferred model can be naturally integrated with existing multi-armed selection strategies in the online interactive collaborating se ing. Empirical studies on two real-world applications, online recommendations of movies and news, demonstrate both the e ectiveness and e ciency of the proposed approach.

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Time lag is a key feature of hidden temporal dependencies within sequential data. In many real-world applications, time lag plays an essential role in interpreting the cause of discovered temporal dependencies. Traditional temporal mining methods either use a predefined time window to analyze the item sequence, or employ statistical techniques to simply derive the time dependencies among items. Such paradigms cannot effectively handle varied data with special properties, e.g., the interleaved temporal dependencies.In this paper, we study the problem of finding lag intervals for temporal dependency analysis. We first investigate the correlations between the temporal dependencies and other temporal patterns, and then propose a generalized framework to resolve the problem. By utilizing the sorted table in representing time lags among items, the proposed algorithm achieves an elegant balance between the time cost and the space cost. Extensive empirical evaluation on both synthetic and real data sets demonstrates the efficiency and effectiveness of our proposed algorithm in finding the temporal dependencies with lag intervals in sequential data.