Towards Social Recommendation System Based on the Data from Microblogs (original) (raw)
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Understanding Microblog Users for Social Recommendation Based on Social Networks Analysis
J. Univers. Comput. Sci., 2012
With the rapid growth of Internet and social networking websites, various services are provided in these platforms. For instance, Facebook focuses on social activities, Twitter and Plurk (which are called microblogs) are both focusing on the interaction of users through short messages. Millions of users enjoy services from these websites which are full of marketing possibilities. Understanding the users can assist companies to enhance the accuracy and efficiency of the target market. In this paper, a social recommendation system based on the data from microblogs is proposed. This social recommendation system is built according to the messages and social structure of target users. The similarity of the discovered features of users and products will then be calculated as the essence of the recommendation engine. A case study included in the paper presents how the recommendation system works based on real data from Plurk.
An Algorithmic approach for recommendation systems for web blogs and microblogs
2020
Now a days, Recommendations system is a pillar for each web portal and application. Recommendation systems are not specific to a single domain, it is applicable in diverse domains. It is used in social networking sites for posts and recommending friends; or e-commercial websites for giving suggestions for the user about products and services; or it’s a movie recommendations based on the user’s interest and suggest the upcoming movies which is well suited for a respective user. At the backend, the machine learning is used to give better recommendations and suggestions. In the paper, various algorithms are discusses with their implementation areas. A comparative study is discussed in the paper and a proposed algorithm which is well suited for the web blogs and the microblogs websites. A proposed approach uses the machine learning methodologies based on the user’s point of interest. User’s hobbies and the likings are the major concerns when the recommendation system recommends the post...
2019
Microblogging platforms constitute a popular means of real-time communication and information sharing. They involve such a large volume of user-generated content that their users suffer from an information deluge. To address it, numerous recommendation methods have been proposed to organize the posts a user receives according to her interests. The content-based methods typically build a text-based model for every individual user to capture her tastes and then rank the posts in her timeline according to their similarity with that model. Even though content-based methods have attracted lots of interest in the data management community, there is no comprehensive evaluation of the main factors that affect their performance. These are: (i) the representation model that converts an unstructured text into a structured representation that elucidates its characteristics, (ii) the source of the microblog posts that compose the user models, and (iii) the type of user's posting activity. To...
In recent years, the edge between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper we represent a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in " coldstart " situations, a problem which has rarely been explored before. A major threat is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental calculation on a large dataset build from the largest Chinese micro blogging service SINA WEIBO and the largest Chinese B2C e-commerce website JINGDONG have given the effectiveness of our proposed framework.