Where to Go on Your Next Trip? Optimizing Travel Destinations Based on User Preferences

Recommendation based on user preferences is a common task for e-commerce websites. New recommendation algorithms are often evaluated by offline comparison to baseline algorithms such as recommending random or the most popular items. Here, we investigate how these algorithms themselves perform and compare to the operational production system in large scale online experiments in a real-world application. Specifically, we focus on recommending travel destinations at Booking.com, a major online travel site, to users searching for their preferred vacation activities. To build ranking models we use multi-criteria rating data provided by previous users after their stay at a destination. We implement three methods and compare them to the current baseline in Booking.com: random, most popular, and Naive Bayes. Our general conclusion is that, in an online A/B test with live users, our Naive-Bayes based ranker increased user engagement significantly over the current online system.Comment: 6 pages, 2 figures in SIGIR 2015, SIRIP Symposium on IR in Practic

Building sentiment lexicons based on recommending services for the Polish language

Sentiment analysis has become a prominent area of research in computer science. It has numerous practical applications; e.g., evaluating customer satisfaction, identifying product promoters. Many methods employed in this task require language resources such as sentiment lexicons, which are unavailable for the Polish language. Such lexicons contain words annotated with their emotional polarization, but the manual creation of sentiment lexicons is very tedious. Therefore, this paper addresses this issue and describes a new method of building sentiment lexicons automatically based on recommending services. Next, the built lexicons were used in the task of sentiment classification

Addressing Human Papillomavirus Vaccination in Primary Care Pediatrics

Human papillomavirus (HPV) is the most common sexually transmitted disease in the United States. Despite most common transmission, HPV immunization in adolescents remains below target rates of 80% as outlined by Healthy People 2020 Objectives. Nearly all individuals will contract HPV during their lifetime. The purpose of this project was to educate providers on successfully promoting HPV immunization in adolescents utilizing evidence-based methods. The health belief model (HBM) was the theoretical underpinning utilized to teach providers on discussions about 9vHPV immunization with parents of adolescents. The practice focused question explored whether an education program using concepts from the HBM would increase provider perception of preparedness on recommending Gardasil 9 immunization in adolescents. Convenience sampling was utilized to recruit participants. There were 9 out of 25 providers that attended the educational in service with 8 completing the continuing education evaluation tool. Participants included providers who are affiliated and hold privileges with the health care system. Survey Monkey was used to analyze the participant evaluations. All the participants found the educational information relevant to increasing their perception of preparedness on recommending Gardasil 9 immunization in adolescents. The findings suggest that providers would benefit from training on recommending HPV immunization in adolescents. Continued training would help enhance timely immunization rates that could decrease cancer rates and reduce associated healthcare cost, in turn promoting population health and positive social change

On recommending hashtags in Twitter networks

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

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

What’s going on in my city? Recommender systems and electronic participatory budgeting

In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities – Cambridge, Miami and New York City–, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area

Time-aware topic recommendation based on micro-blogs

Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com