Bag-of-Features Tagging Approach for a Better Recommendation with Social Big Data

摘要

The interests of users are always important for personalized content recommendations on friendships, events and media content from the social big data. However, those interests may not be specified, which makes the recommendations challenging. One of the possible solutions is to analyze the user's interests from the shared content, especially images with manually annotated tags. They are shared on online social networks such as Flickr and Instagram. However, the accuracy of the recommendation is greatly affected by the accuracy of the tag, which is not always reliable. This paper demonstrates how a bag-of-features (BoF)-based tagging approach can help to improve the accuracy of recommendations using an unsupervised algorithm. A set of auxiliary tags is used to represent user interests and, hence, the recommendation. The approach is evaluated with over 500 user and 200k images from Flickr. It is proven that by BoF tagging (BoFT), friendship recommendation is possible without friendship/tag information and the recall and the precision rate are improved by about 50% over using user tags.