Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries 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 propose 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 'cold-start' situations, a problem which has rarely been explored before. A major challenge 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 results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

From Underground to Elite: Egyptian Bloggers before and after the 2011 Uprising

This research looks at how the shift in the status of Egyptian bloggers from underground dissident voices to mainstream political and media players affected the plurality they add to the public space for discourse in Egypt’s authoritarian settings. The role of the internet – and more recently social media and bloggers – in democratic transition has been studied by various media scholars since the introduction of the worldwide web and especially after the Egyptian and Tunisian uprisings of 2011. But no work has been done to study how bringing those once-underground bloggers into the public and media spotlight affected the nature of the blogosphere and the bloggers themselves. Star bloggers were not only covered by the media after January 25th, 2011, they also started joining the media as column writers; a move that had various effects on them and the blogosphere but was never examined in media studies. The plurality the blogosphere adds to the Egyptian public space for discourse in light of those changes as well as in light of the financial and practical sustainability of blogging was hence never looked at in a context similar to Egypt’s. Guided by modified theories of the public sphere and theories of hegemony and manufacturing consent, I look at whether bloggers have been co-opted into the historical bloc in the process of renewing the social order and how this affects them and the online sphere. Also, guided by theories of power and media elites, I look at bloggers’ backgrounds to assess whether they come from power elites and are transforming into media elites, thus limiting the plurality of the online sphere. Finally, guided by theoretical works on institutionalizing and commercializing the internet, I look at how those shifts into mainstream affect the independence and freedom of the blogs and microblogs. The research uses a comparative study to assess how those changes affect prominent versus less prominent bloggers and compare their backgrounds. The study uses quantitative content analysis and framing analysis of chosen media outlets and interviews with bloggers, marketeers and media professionals. The findings trace an increase in media coverage of bloggers post January 25th, 2011, especially in the prominent bloggers category, and an overall positive framing of bloggers post the uprising. This led to the mainstreaming of bloggers into the media as well as public work, which had various implications on the freedom they had over their content and voice, both online and offline. It also points to a dramatic decrease in bloggers’ activity on their blogs in favour of mainstream and social media and due to star bloggers becoming more career-oriented and their failure to make blogs financially sustainable. The findings also indicate that more prominent bloggers seem to come from more elite backgrounds than others and enjoy luxuries that allow them the time, technology and security to post online. This research concludes that the shifts in bloggers’ status post-January 25th have limited the plurality they add to the discourse in Egypt.

A novel concept-level approach for ultra-concise opinion summarization

The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users communicate, exchange, and share opinions. The exploitation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed. Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of state-of-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.

Semi-automatic keyword based approach for FIRE 2016 Microblog Track

This paper describes our semi-automatic keyword based approach for the four topics of Information Extraction from Microblogs Posted during Disasters task at Forum for Information Retrieval Evaluation (FIRE) 2016. The approach consists three phases.