Time-Ordered Collaborative Filtering for News Recommendation

Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested in.Therefore,various news recommender systems were built.In the news recommendation,these news articles read by a user is typically in the form of a time sequence.However,traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors.Therefore,the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read.To solve this problem,this paper proposes a time-ordered collaborative filtering recommendation algorithm(TOCF),which takes the time sequence characteristic of user behaviors into account.Besides,a new method to compute the similarity among different users,named time-dependent similarity,is proposed.To demonstrate the efficiency of our solution,extensive experiments are conducted along with detailed performance analysis.

Mining the interests of Chinese microbloggers via keyword extraction

Microblogging provides a new platform for com- municating and sharing information among Web users. Users can express opinions and record daily life using microblogs. Microblogs that are posted by users indicate their interests to some extent. We aim to mine user interests via keyword extraction from microblogs. Traditional keyword extraction methods are usually designed for formal documents such as news articles or scientific papers. Messages posted by mi- croblogging users, however, are usually noisy and full of new words, which is a challenge for keyword extraction. In this paper, we combine a translation-based method with a frequency-based method for keyword extraction. In our ex- periments, we extract keywords for microblog users from the largest microblogging website in China, Sina Weibo. The re- suits show that our method can identify users＇ interests accu- rately and efficiently.

Personalization Method of E-Catalog Based on User Interesting Degree

The user interesting degree evaluation index is designed to fulfill the users＇ real needs, which includes the user＇ attention degree of commodity, hot commodity and preferential commodity. User interesting degree model （UIDM） is constructed to justify the value of user interesting degree; the personalization approach is presented; operations of add and delete nodes （branches） are covered in this paper. The improved e-catalog is more satisfied to users＇ needs and wants than the former e-catalog which stands for enterprises, and the improved one can complete the recommendation of related products of enterDriscs.

Generating timeline summaries with social media attention

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users＇ collective interests into considerations to generate timelines. We consider utilizing social media attention to address these two problems due to the facts： 1） social media is an important pool of real users＇ collective interests; 2） the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user＇s collective interests which are learnt from social media into a unified timeline generation algorithm. We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics. We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presen- tation of timelines, i.e., phase based timelines, which can potentially improve user experience.

A Novel Popularity Extraction Method Applied in Session-Based Recommendation

Popularity plays a significant role in the recommendation system. Traditional popularity is only defined as a static ratio or metric (e.g., a ratio of users who have rated the item and the box office of a movie) regardless of the previous trends of this ratio or metric and the attribute diversity of items. To solve this problem and reach accurate popularity, we creatively propose to extract the popularity of an item according to the Proportional Integral Differential (PID) idea. Specifically, Integral (I) integrates a physical quantity over a time window, which agrees with the fact that determining the attributes of items also requires a long-term observation. The Differential (D) emphasizes an incremental change of a physical quantity over time, which coincidentally caters to a trend. Moreover, in the Session-Based Recommendation (SBR) community, many methods extract session interests without considering the impact of popularity on interest, leading to suboptimal recommendation results. To further improve recommendation performance, we propose a novel strategy that leverages popularity to enhance the session interest (popularity-aware interest). The proposed popularity by PID is further used to construct the popularity-aware interest, which consistently improves the recommendation performance of the main models in the SBR community. For STAMP, SRGNN, GCSAN, and TAGNN, on Yoochoose1/64, the metric P@20 is relatively improved by 0.93%, 1.84%, 2.02%, and 2.53%, respectively, and MRR@20 is relatively improved by 3.74%, 1.23%, 2.72%, and 3.48%, respectively. On Movieslen-1m, the relative improvements of P@20 are 7.41%, 15.52%, 8.20%, and 20.12%, respectively, and that of MRR@20 are 2.34%, 12.41%, 20.34%, and 19.21%, respectively.