Social voting is becoming the new reason behind social recommendation these days. It helps in providing accurate recommendations with the help of factors like social trust etc. Here we propose Matrix factorization (MF) and nearest neighbor-based recommender systems accommodating the factors of user activities and also compared them with the peer reviewers, to provide a accurate recommendation. Through experiments we realized that the affiliation factors are very much needed for improving the accuracy of the recommender systems. This information helps us to overcome the cold start problem of the recommendation system and also y the analysis this information was much useful to cold users than to heavy users. In our experiments simple neighborhood model outperform the computerized matrix factorization models in the hot voting and non hot voting recommendation. We also proposed a hybrid recommender system producing a top-k recommendation inculcating different single approaches.
Abstract-Over the decades, numerous kinds of knowledge discovering and sharing of the data techniques are playing a major role to reach the information quickly. Among these since last few years, social networks or media and own blogging are playing a major in sharing the personal information, updating the status, tagging the location and many more features. These data are considered to examine and the acceptance for emergency services to respond with the information gathered from the social network. Taking this into the consideration, proposed an algorithm to find out the location of the person based upon the information shared. This is implemented on a most popular social media twitter to identify the tweets.
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