Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-based recommendation, as an emerging research area, uses social information to help mitigate the data sparsity and cold start problems, and it has been demonstrated that the social-based recommendation algorithms can efficiently improve the recommendation performance. However, few of the existing algorithms have considered using multiple types of relations within one social network. In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a Social Collaborative Filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilize multiple types of relations in a heterogeneous social network. In addition, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information. The experimental results on two real-world data sets, DBLP (a typical heterogeneous information network) and Meetup (a typical event based social network) show the effectiveness and efficiency of our algorithm

Event Organization 101: Understanding Latent Factors of Event Popularity

The problem of understanding people's participation in real-world events has been a subject of active research and can offer valuable insights for human behavior analysis and event-related recommendation/advertisement. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in three major cities around the world. We have conducted modeling analysis of four contextual factors (spatial, group, temporal, and semantic), and also developed a group-based social influence propagation network to model group-specific influences on events. By combining the Contextual features And Social Influence NetwOrk, our integrated prediction framework CASINO can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Evaluations demonstrate that our CASINO framework achieves high prediction accuracy with contributions from all the latent features we capture.Comment: International AAAI Conference on Web and Social Media (ICWSM) 2017 https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/1557

Is That Twitter Hashtag Worth Reading

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.Comment: 10 pages, 6 figures, Presented at the Third International Symposium on Women in Computing and Informatics (WCI-2015

Study of event recommendation in event-based social networks

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Maria Salamó Llorente[en] Recommendations are in our every day life: streaming services, social media, web pages... are adopting and using recommender algorithms. Recommendation algorithms benefit both parts: clients can find more easily products that they like, and the companies make more benefits because clients use their services more. The recommendation problem presented in this work is a non-traditional variant of this problem as it recommends events. Events, unlike books or videos, cannot be recommended in the same way, because users cannot rate an event until the day it happens, and then no new users can rate it again after that. This magnifies a problem called “cold start problem” where every new event has no ratings, which greatly complicates the recommendation problem. This work studies Event Recommendation for a social media called Meetup 1 where users can attend a selection of events created by the community. Although users do not leave a rating of the event, we have a signal called RSVP 2 , which is a non-obligatory mark on whether the user has the intention to attend the event or not. In this work we will be exploring how different recommender algorithms perform to recommend events based on RSVPs and also propose three new algorithms. The analysis will be done with 5 datasets extracted from Meetup during the months between November 2017 and April 2018. The results show that hybrid versions containing collaborative and contextual-aware algorithms rank the best among all the algorithms tested

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Culture and disaster risk management - citizens’ reactions and opinions during Citizen Summit in Lisbon, Portugal

The analyses and results in this document are based on the data collected during the fifth Citizen Summit held in Lisbon, Portugal on April 14th 2018. Like the previous four Citizen Summits held in Romania, Malta, Italy and Germany, this Citizen Summit was designed as a one-day event combining public information with feedback gathering through different methods of data collection. In the morning session, the event started with a presentation of the CARISMAND project and its main goals and concepts, and the planned CARISMAND Toolkit functionalities. Then, overall 27 questions with pre-defined answer options were posed to the audience and responses collected via an audience response system. As in the previous Citizen Summits, all questions in this part of the event aimed to explore citizens’ attitudes, perceptions and intended behaviours related to disaster risks. Comparing and contrasting the respective results of all six Citizen Summits in the final synthesised analysis will aim to provide additional insight into cultural factors that may affect disaster-related preparedness and response. Between these questions, additional presentations where held that informed the audience about state-of-the-art disaster preparedness and response topics (e.g., large-scale disaster scenario exercises, use of social media, and mobile phone apps). Furthermore, this last round of Citizen Summits was organised and specifically designed to discuss and collect feedback on recommendations for citizens, which have all been formulated on the basis of Work Packages 2-8 results and in coordination with the Work Package 11 brief. These Toolkit recommendations are envisaged to form one of the core elements of the Work Package 9 CARISMAND Toolkit. Additionally, following the cyclical design of CARISMAND events (and wherever meaningful and possible), they “mirror” the respective recommendations for practitioners, which were discussed in the last (third) CARISMAND Stakeholder Assembly held in Lisbon in February 2018, and they are structured in two, main “sets”: A. Developing a personal “culture of preparedness” B. Taking part in disaster preparedness and response activities. These two sets of recommendations were also presented in detail during the morning session to the participating citizens. In the afternoon session, small moderated group discussions of approximately 2 hours duration were held, which aimed to gather the citizens’ direct feedback on the two sets of Toolkit recommendations presented in the morning, following a detailed discussion guideline. For a detailed overview of all questions asked and topics discussed please see Appendix A. Overall, 102 citizens participated in the Portugal event. The total sample shows a relatively even gender and age distribution, which is unsurprising given the target quotas that were requested from the recruiting local market research agency. The lower number of senior citizens aged 65 and above was expected and reflects mobility issues. Participants were asked about three key aspects of experience of disasters and disaster risk perception that could potentially have an impact on how other questions were answered. More than nine out of ten respondents (92.8%) indicated that they, or a close friend or family member, have experienced a disaster, more than half (56.7%) felt that they are currently living in an area that is specifically prone to disasters, and 57.8% answered that they know other people in the area where they live who they think are particularly vulnerable or exposed to disasters. Slight gender and age-related differences in the responses to these questions were found to be not statistically significant (p>=.05). The rest of this report presents the results of the fifth CARISMAND Citizen Summit and is structured in five main sections. After this introduction, the second section will provide an overview of the different methods applied. The third section, based on the quantitative data collected via the audience response system, presents the results from questions on general disaster risk perceptions, disaster preparedness, and behaviours in disaster situations with a particular focus on the use of mobile phone apps and social media. In the fourth section, based on the qualitative data collected in the ten discussion groups, the analyses will provide detailed insight into the participants’ feedback on the two sets of recommendations for citizens presented in the morning session. The final section compares and contrasts the results from sections 3 and 4, draws conclusions, and presents proposed changes and amendments to the Work Package 9 Toolkit recommendations based on the participating citizens’ suggestions.The project was co-funded by the European Commission within the Horizon2020 Programme (2014-2020).peer-reviewe

Culture and disaster risk management - citizens’ reactions and opinions during Citizen Summit in Utrecht, Netherlands

The analyses and results in this document are based on the data collected during the sixth Citizen Summit held in Utrecht, the Netherlands on May 12th, 2018. Like the previous five Citizen Summits held in Romania, Malta, Italy, Germany, and Portugal, this Citizen Summit was designed as a one-day event combining public information with feedback gathering through different methods of data collection. In the morning session, the event started with a presentation of the CARISMAND project and its main goals and concepts, and the planned CARISMAND Toolkit functionalities. Then, overall 27 questions with pre-defined answer options were posed to the audience and responses collected via an audience response system. As in the previous Citizen Summits, all questions in this part of the event aimed to explore citizens’ attitudes, perceptions, and intended behaviours related to disaster risks. Comparing and contrasting the respective results of all six Citizen Summits in the final synthesised analysis (Deliverable D5.9) will aim to provide additional insight into cultural factors that may affect disaster-related preparedness and response. Between these questions, additional presentations were held that informed the audience about state-of-the-art disaster preparedness and response topics (e.g., large-scale disaster scenario exercises, use of social media, and mobile phone apps). Furthermore, this last round of Citizen Summits was organised and specifically designed to discuss and collect feedback on recommendations for citizens, which have all been formulated on the basis of Work Packages 2-10 results and in coordination with the Work Package 11 brief. These Toolkit recommendations are envisaged to form one of the core elements of the Work Package 9 CARISMAND Toolkit. Additionally, following the cyclical design of CARISMAND events (and wherever meaningful and possible), they “mirror” the respective recommendations for practitioners, which were discussed in the last (third) CARISMAND Stakeholder Assembly held in Lisbon in February 2018, and they are structured in two, main “sets”: A. Developing a personal “culture of preparedness” B. Taking part in disaster preparedness and response activities. These two sets of recommendations were also presented in detail during the morning session to the participating citizens. In the afternoon session, small moderated group discussions of approximately 2 hours’ duration were held, which aimed to gather the citizens’ direct feedback on the two sets of Toolkit recommendations presented in the morning, following a detailed discussion guideline. For a detailed overview of all questions asked and topics discussed, please see Appendix A. Overall, 89 citizens participated in the Netherlands’ event. The total sample shows a relatively even gender and age distribution, which is unsurprising given the target quotas that were requested from the recruiting local market research agency. The lower number of senior citizens aged 65 and above was expected and reflects mobility issues. Participants were asked about three key aspects of experience of disasters and disaster risk perception that could potentially have an impact on how other questions were answered. Almost three out of five respondents (58.1%) indicated that they, or a close friend or family member, have experienced a disaster, whereas only one out of five (20.7%) felt that they are currently living in an area that is specifically prone to disasters, but 44.2% answered that they know other people in the area where they live who they think are particularly vulnerable or exposed to disasters. Slight gender- and age-related differences in the responses to these questions were found to be not statistically significant (p>=.05). The rest of this report is structured in five main sections: After this introduction, the second section will provide an overview of the different methods applied. The third section, based on the quantitative data collected via the audience response system, presents the results from questions on general disaster risk perceptions, disaster preparedness, and behaviours in disaster situations with a particular focus on the use of mobile phone apps and social media. In the fourth section, based on the qualitative data collected in the ten discussion groups, the analyses will provide detailed insight into the participants’ feedback on the two sets of recommendations for citizens presented in the morning session. The final section compares and contrasts the results from sections 3 and 4, draws conclusions, and presents proposed changes and amendments to the Work Package 9 Toolkit recommendations based on the participating citizens’ suggestions.The project was co-funded by the European Commission within the Horizon2020 Programme (2014-2020).peer-reviewe

Localized Events in Social Media Streams: Detection, Tracking, and Recommendation

From the recent proliferation of social media channels to the immense amount of user-generated content, an increasing interest in social media mining is currently being witnessed. Messages continuously posted via these channels report a broad range of topics from daily life to global and local events. As a consequence, this has opened new opportunities for mining event information crucial in many application domains, especially in increasing the situational awareness in critical scenarios. Interestingly, many of these messages are enriched with location information, due to the wide- spread of mobile devices and the recent advancements of today’s location acquisition techniques. This enables location-aware event mining, i.e., the detection and tracking of localized events. In this thesis, we propose novel frameworks and models that digest social media content for localized event detection, tracking, and recommendation. We first develop KeyPicker, a framework to extract and score event-related keywords in an online fashion, accounting for high levels of noise, temporal heterogeneity and outliers in the data. Then, LocEvent is proposed to incrementally detect and track events using a 4-stage procedure. That is, LocEvent receives the keywords extracted by KeyPicker, identifies local keywords, spatially clusters them, and finally scores the generated clusters. For each detected event, a set of descriptive keywords, a location, and a time interval are estimated at a fine-grained resolution. In addition to the sparsity of geo-tagged messages, people sometimes post about events far away from an event’s location. Such spatial problems are handled by novel spatial regularization techniques, namely, graph- and gazetteer-based regularization. To ensure scalability, we utilize a hierarchical spatial index in addition to a multi-stage filtering procedure that gradually suppresses noisy words and considers only event-related ones for complex spatial computations. As for recommendation applications, we propose an event recommender system built upon model-based collaborative filtering. Our model is able to suggest events to users, taking into account a number of contextual features including the social links between users, the topical similarities of events, and the spatio-temporal proximity between users and events. To realize this model, we employ and adapt matrix factorization, which allows for uncovering latent user-event patterns. Our proposed features contribute to directing the learning process towards recommendations that better suit the taste of users, in particular when new users have very sparse (or even no) event attendance history. To evaluate the effectiveness and efficiency of our proposed approaches, extensive comparative experiments are conducted using datasets collected from social media channels. Our analysis of the experimental results reveals the superiority and advantages of our frameworks over existing methods in terms of the relevancy and precision of the obtained results

A Topic Recommender for Journalists

The way in which people acquire information on events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to news stimulates a demand for additional information which is often met through online encyclopedias, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests the readers. The goal of this paper is to present a recommender system, What to Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter and Wikipedia, either not covered or poorly covered in the published news articles