Research Of Adaptive Information Recommendation Mechanism Based On Social Media

In the Internet, it is very convenient to release some information, which makes the quantity of information grow explosively. So much information cannot be skim through, and of course it is more impossible that a user is hoping to find interested things. Traditionally search engines only present all users the same sorted results, and can not provide the corresponding services according to different users' interest preferences. Information explosion leads to reverse utilization, which is called "information overload". To solve the problem of "information overload" on the Internet, recommender system is proposed as a kind of intelligent agent system, which can automatically recommend the resources to users from the Internet that meet their interest preferences or demands.In the current Web 2.0, the emergence of social media has made that each user can not only browse the Web, but also create and disseminate information, which leads to information overload more serious. The traditional recommender systems acquire users' interests by letting users answer questions or active customization and recommend relevant items. However, users' interests will change as time passes by. For the point, this paper puts forward an adaptive recommendation mechanism, to make timely follow-up of user interest' change, and recommend users interested resources. Social media has various forms, such as BBS, Blog, content community, social network and so on. In these forms, a user can post or repost an article, and other users can read or comment on it, these comments themselves can be read and commented by other users. From the users' comments, we can observe the current topic of common interest of users. The traditional content-based recommenders usually recommend related articles based on the original content. However, as we know, with users' discussion going on, the topic will also be changing, namely the users' interest will also be changing. At this moment, if recommendation is only based on the original, returned articles will be of no interest to users, which will further reduce user satisfaction. Therefore, in this paper, we consider combining user comments with the original to build a topic profile, and then utilize the topic profile to select some related articles. On the basis of our observation, the impact of each comment on recommendation varies according to its quality. In particular, some comments reflect insightful opinions on the original, which provides balanced views from both readers and authors, while some are meaningless discussions. Differentiating the contribution of each comment is important to utilize them properly in guiding the topic evolution for recommendation in social media. Here, we extract structural, semantic, and authority information carried by the comments to differentiate the importance of every comment. Analyzing coverage transmission on the Internet, we can find that it has four features as follows:reposting coincidence, reporting coincidence, containing superposition and tracking superposition. These characteristics make the content-based recommendation system has a serious problem-repeat recommendation, namely the recommended contains the same information as the original, which will increase users'reading burden. Hence, a method is proposed to explain the logical relationship between recommend articles and the original (including generalization, specialization and repetition), in order to reduce repetitive content and recommend the articles that meet user demands.The first part introduces the research background, research purpose, significance, and some basic concepts involved in this paper. About recommender system, it first introduces the definition and four common methods, including content-based recommendation, collaborative filtering recommendation, mixed recommendation and data mining based recommendation, then for these methods respectively exemplifies a system to explain its working mode, and summarizes the evaluation standards of recommender system. Besides, it also introduces the concept of social media and its features, compared with traditional media. At last the contributions are listed as follows:(1) This study is to take the lead in utilizing user comments to assist the information recommendation service at home and abroad. It provides a new idea for adaptive information recommendation in social media and extends the study of information recommendation from traditional static media in Web 1.0 to social media in Web 2.0.(2) In order to make the best use of engaging interaction among users in social media, we design a mechanism, which mines information from user comments by using the graph theory, to accurately capture users' concern about some event. Then, it is combined with the original content, which can balance views of both authors and readers.(3) In order to reduce user cognitive burden, we put forward a creative approach to generate hints to indicate the logical relationship between articles based on the information entropy. Thus, we can judge the relationships between recommended articles and the original posting. In addition, the research can be widely applied to text analysis, for example, presentation of search engine results, based-content advertising.setting, etc.The second part introduces the research foundation and background. First of all, for our experimental objects, namely news and Blog, their existing works are summarized. News recommendation is presented from two aspects of existing business recommenders and academic research. Secondly, for a fundamental challenge in this paper, that is topic divergence, the development of topic detection and tracking (TDT) is in the summary. Finally, some involved theories are briefly introduced, such as language model (LM), PageRank algorithm, information entropy, T-test, and so on.The third part is the core, which introduces the design of our adaptive recommendation mechanism. First, it presents a framework for recommendation and briefly introduces the process. Then, each module expounds in the framework. The authority for each user is calculated through modeling the relations among users, which include quotation and reply. In the whole community, it constructs graph models when a user replies to another user's posting or a user quotes another user's posting separately, and employs a variant of the PageRank algorithm. Next, comment's weight is calculated. Here, we still use the graph model, however, differently, the model is built on the relationships among users' comments, including semantic, quotation and reply. The content relation means the semantic similarity between comments, and the quotation or reply means that a comment quotes another one, or replies to another one. After building the models, another variant of the PageRank algorithm is used to calculate the weight of every comment. The quality of a comment can be determined by its authority and itself, therefore, it combines user authority with comment weight to get the final weight for each comment. Then, this information along with the entire discussion thread is fed into a synthesizer to construct a topic profile, which balances the perspectives of both authors and readers. With the topic profile constructed, the retriever returns an ordered list of articles with decreasing relevance to the topic. Finally, we utilize the information entropy to explain the logical relationships between returned articles and the original, and recommend articles with users' interest.The fourth part is about experimental design and analysis. It presents the system development environment, experimental data acquisition and its details. Experimental data includes two synthetic data sets:one is news, the other is Blog. Because we obtain the entire data in a web page, the page is first parsed to extract our required parts. It also introduces the selection of evaluation standards. Here, besides some common indexes, we introduce a metric of innovativeness to measure the topic diversity of returned articles. Then, we design a series of experiments to test our proposal:1) we compare our work to two baseline works. The results show that our approach performs significantly better than the baseline methods for both news and Blog data sets; 2) we study the effect of user authority and its integration to comment weighting and the result shows that with the assistance of user authority and comments, the recommendation precisions are improved for news and Blog; 3) we investigate the effect of the semantic and structural relations among comments, i.e. semantic similarity, reply, and quotation. The result indicates that semantic contents of user comments can play a fairly different role in a different form of social media. For the case of news, incorporating content information adversely affects recommendation precision. On the other hand, when we test the Blog data set, the trend is the opposite, i.e. content similarity does contribute to retrieval performance positively; 4) we evaluate the performance gain obtained from interpreting recommendation.The last part summarizes the research work and looks into the future research. It makes a summary of the overall design and implementation of adaptive recommendation system based on social media. Furthmore, it also points out some deficiencies and gives some future directions.