Most ranking-oriented collaborative filtering (CF) algorithms have two limitations. Firstly, they only consider the accordance of user preferences but ignore the degrees and popularities of user preferences when computing user similarities. Secondly, an intermediate step is necessary to formulate the value function, for preference prediction and aggregation in greedy algorithms to derive recommendation lists. To address these problems, we propose a Degree-Popularity weighting scheme integrating TF-IDF to weight the degrees and popularities of user pairwise relative preferences, and compute similarities between users based on weighted Kendall Tau rank correlation coefficient. Preference aggregations and predictions are directly formulated and the recommendation lists are consequently derived by applying the Schulze method. We conduct extensive experiments on two movie datasets under NDCG evaluation, implying advantageous results in comparison with the state-of-the-art CF algorithms.

Due to such natures as content diversity, dynamic evolution ,and so on, its difficult to get high quality subtopics for web texts and topics by traditional topic detection and tracking models. An algorithm of subtopic partition based on absorbing Markov chain is proposed to address this issue. The algorithm firstly gathers the topic keywords clustered by the web pages to generate subtopics, then derived subtopics based on the absorbing Markov chain. The experimental results show that the algorithm performs well in terms of both significance and diversity.

This work conducts a contrastive study on the topics of specific events from microblog and news media. Firstly, we use LDA to extract topics from the two media, and then define three indexes: attention factor, diversity factor and evolution factor for an improved topic discrepancy calculation. Finally, we chose four events of different types for experiments and analysis. The results show: 1) There are more comment topics appearing on microblog with close attention factors in contrast to a high proportion of factual topics with varied attention factors in the news media. 2) In both microblog and news media, diversity factor of words used in the comment topics is bigger than that in factual topics. 3) In microblog, comment topics last longer with consistent contents, while the factual topics does so in the news media.

Social Network is a new medium of exchanging information on line. Take Renren.com as an example, a myriad of young people, especially students, talk about interesting topics on this platform. People are connected for many reasons, such as studying in same college, working in same company, having interest in common. And the network nodes in Renren.com are probably joined together in groups according to the property of users department or school. In this article, the real-world network data is collected from Renren.com in the first place, and then the CNM algorithm is utilized to validate assumptions mentioned above. Based on the structure of Social Network, an improved method for discovering community structure is proposed, which outperforms the CNM in terms of accuracy. The community structure detected in the social network shows the different characteristics of each department or school in college.

The effectiveness of information retrieval from the web largely depends on whether users can properly describe their information needs in the queries issue to the search engines. However, many search queries are short, ambiguous or even noisy. Query recommendation technology help users refine their queries and describe the information needs clearly. In order to obtain high quality query recommendations, query candidates are at first generated with a random walk strategy adopted on Query-URL bipartite graph. Snippet click behavior information is then adopted to re-rank the candidate lists infavor of the queries representing user intents. Learning based algorithms are finally utilized to reduce the possible noises in recommendations. Experiment on practical search user behavior data shows the effectiveness of the proposed method.

As an important category of traditional work in search engine evaluation, user satisfaction evaluation has many differences from traditional relevance measurement evaluation. User satisfaction is a more user-centered evaluation, providing a global and systematic evaluation to the performance of search engine. This paper describes the relationship between search engine user behavior and user satisfaction evaluation. We designs an experiment with the premise of avoiding impacting user searching experiences, through which we collected query-level explicit judgments of user satisfaction and user behavior log, then analyzes the collected data to elicit valuable conclusions. The findings provide insights into the improvement of the performance of search engine and the amelioration of user searching experiences.

Under the environment of big data, traditional database-centered data processing architecture cannot meet the requirement of high concurrency of read/write requests. At the same time, serial usages of data limit the effectiveness of data processing. This paper describes an effective on-line data process and service framework based on memory. This framework uses multi-index data access method and pub/sub data control mechanism to improve the effectiveness of data processing while reducing the interaction with the database. Experimental results show that the memory based on-line data process and service framework can significantly improve the response speed of database and shorten the latency of data processing.