Xiamen University

About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.

Modeling and broadening temporal user interest in personalized news recommendation

Abstract: An experimental study on user interest evolution in real-world recommender systems.Integrating the long-term and short-term reading preferences of users.Selecting news from the user-item affinity graph using absorbing random walk model.Extensive empirical experiments on news data obtained from popular news websites. User profiling is an important step for solving the problem of personalized news recommendation. Traditional user profiling techniques often construct profiles of users based on static historical data accessed by users. However, due to the frequent updating of news repository, it is possible that a user's fine-grained reading preference would evolve over time while his/her long-term interest remains stable. Therefore, it is imperative to reason on such preference evaluation for user profiling in news recommenders. Besides, in content-based news recommenders, a user's preference tends to be stable due to the mechanism of selecting similar content-wise news articles with respect to the user's profile. To activate users' reading motivations, a successful recommender needs to introduce "somewhat novel" articles to users.In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that a user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen as the recommended candidates based on the short-term user profile. We further propose to select news items from the user-item affinity graph using absorbing random walk model to increase the diversity of the recommended news list. Extensive empirical experiments on a collection of news data obtained from various popular news websites demonstrate the effectiveness of our method.

A well-resolved uudd cyclic water tetramer in the crystal host of [Cu(adipate)(4,4-bipyridine)].(H2O)2.

Abstract: A well-resolved uudd cyclic water tetramer was reported in the crystal host of [Cu(adipate)(4,4-bipyridine)]·(H2O)2, showing the contribution of the water cluster to the stability of the crystal host and the role of cooperative association of the water cluster and the crystal host in the formation of the water cluster.

Electrochemical C-H/N-H Functionalization for the Synthesis of Highly Functionalized (Aza)indoles.

Abstract: Indoles and azaindoles are among the most important heterocycles because of their prevalence in nature and their broad utility in pharmaceutical industry. Reported herein is an unprecedented noble-metal- and oxidant-free electrochemical method for the coupling of (hetero)arylamines with tethered alkynes to synthesize highly functionalized indoles, as well as the more challenging azaindoles.