Personalizing image tags is a relatively new and growing area of research, and in order to advance this research community, we must review and challenge the de-facto standard of defining tag importance. We believe that for greater progress to be made, we must go beyond tags that merely describe objects that are visually represented in the image, towards more user-centric and subjective notions such as emotion, sentiment, and preferences. We focus on the notion of user preferences and show that the order that users list tags on images is correlated to the order of preference over the tags that they provided for the image. While this observation is not completely surprising, to our knowledge, we are the first to explore this aspect of user tagging behavior systematically and report empirical results to support this observation. We argue that this observation can be exploited to help advance the image tagging (and related) communities. Our contributions include: 1.) conducting a user study demonstrating this observation, 2.) collecting a dataset with user tag preferences explicitly collected.
With regards to personalization in image tagging, in the work by @cite_17 , they assume that tag mentioned are preferred to those unmentioned. This is similar to our assumption but they treat the tags that appear on a tagging list equally, which our work here suggests not to do. In the work by @cite_3 they also treat tags as essentially structureless entities (bag of words), and other work on personalization similarly treat user provided tag lists as such @cite_3 @cite_7 . To our knowledge, we are the first to suggest that these user provided list should be treated as having structure.
@cite_7 @cite_3 @cite_17
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Despite all of the advantages of tags as an easy and flexible information management approach, tagging is a cumbersome task. A set of descriptive tags has to be manually entered by users whenever they post a resource. This process can be simplified by the use of tag recommendation systems. Their objective is to suggest potentially useful tags to the user. We present a hybrid tag recommendation system together with a scalable, highly efficient system architecture. The system is able to utilize user feedback to tune its parameters to specific characteristics of the underlying tagging system and adapt the recommendation models to newly added content. The evaluation of the system on six real-life datasets demonstrated the system’s ability to combine tags from various sources (e.g., resource content or tags previously used by the user) to achieve the best quality of recommended tags. It also confirmed the importance of parameter tuning and content adaptation. A series of additional experiments allowed us to better understand the characteristics of the system and tagging datasets and to determine the potential areas for further system development. Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning. In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML PKDD Discovery Challenge 2009 for graph-based tag recommendation.