PREDICTION IN SOCIAL MEDIA FOR MONITORING AND RECOMMENDATION

Social media including blogs and microblogs provide a rich window into user online activity. Monitoring social media datasets can be expensive due to the scale and inherent noise in such data streams. Monitoring and prediction can provide significant benefit for many applications including brand monitoring and making recommendations. Consider a focal topic and posts on multiple blog channels on this topic. Being able to target a few potentially influential blog channels which will contain relevant posts is valuable. Once these channels have been identified, a user can proactively join the conversation themselves to encourage positive word-of-mouth and to mitigate negative word-of-mouth. Links between different blog channels, and retweets and mentions between different microblog users, are a proxy of information flow and influence. When trying to monitor where information will flow and who will be influenced by a focal user, it is valuable to predict future links, retweets and mentions. Predictions of users who will post on a focal topic or who will be influenced by a focal user can yield valuable recommendations. In this thesis we address the problem of prediction in social media to select social media channels for monitoring and recommendation. Our analysis focuses on individual authors and linkers. We address a series of prediction problems including future author prediction problem and future link prediction problem in the blogosphere, as well as prediction in microblogs such as twitter. For the future author prediction in the blogosphere, where there are network properties and content properties, we develop prediction methods inspired by information retrieval approaches that use historical posts in the blog channel for prediction. We also train a ranking support vector machine (SVM) to solve the problem, considering both network properties and content properties. We identify a number of features which have impact on prediction accuracy. For the future link prediction in the blogosphere, we compare multiple link prediction methods, and show that our proposed solution which combines the network properties of the blog with content properties does better than methods which examine network properties or content properties in isolation. Most of the previous work has only looked at either one or the other. For the prediction in microblogs, where there are follower network, retweet network, and mention network, we propose a prediction model to utilize the hybrid network for prediction. In this model, we define a potential function that reflects the likelihood of a candidate user having a specific type of link to a focal user in the future and identify an optimization problem by the principle of maximum likelihood to determine the parameters in the model. We propose different approximate approaches based on the prediction model. Our approaches are demonstrated to outperform the baseline methods which only consider one network or utilize hybrid networks in a naive way. The prediction model can be applied to other similar problems where hybrid networks exist

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

A novel data analytic model for mining user insurance demands from microblogs

This paper proposes a method based on LDA model and Word2Vec for analyzing Microblog users' insurance demands. First of all, we use LDA model to analyze the text data of Microblog user to get their candidate topic. Secondly, we use CBOW model to implement topic word vectorization and use word similarity calculation to expand it. Then we use K-means model to cluster the expanded words and redefine the topic category. Then we use the LDA model to extract the keywords of various insurance information on the “Pingan Insurance” website and analyze the possibility of users with different demands to purchase various types of insurance with the help of word vector similarity. Finally, the validity of the method in this paper is verified against Microblog user information. The experimental results show that the accuracy, recall rate and F1 value of the LDA-CBOW extending method have been proposed compared with that of the traditional LDA model, respectively, which proves the feasibility of this method. The results of this paper will help insurance companies to accurately grasp the preferences of Microblog users, understand the potential insurance needs of users timely, and lay a foundation for personalized recommendation of insurance products

GEMRec: A graph-based emotion-aware music recommendation approach

© Springer International Publishing AG 2016. Music recommendation has gained substantial attention in recent times. As one of the most important context features,user emotion has great potential to improve recommendations,but this has not yet been sufficiently explored due to the difficulty of emotion acquisition and incorporation. This paper proposes a graph-based emotion-aware music recommendation approach (GEMRec) by simultaneously taking a user’s music listening history and emotion into consideration. The proposed approach models the relations between user,music,and emotion as a three-element tuple (user,music,emotion),upon which an Emotion Aware Graph (EAG) is built,and then a relevance propagation algorithm based on random walk is devised to rank the relevance of music items for recommendation. Evaluation experiments are conducted based on a real dataset collected from a Chinese microblog service in comparison to baselines. The results show that the emotional context from a user’s microblogs contributes to improving the performance of music recommendation in terms of hitrate,precision,recall,and F1 score

Enhancing Internet-scale Video Service Deployment Using Microblog-based Prediction

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