Collaborative recommender is the most popular recommendation technique nowadays and it mainly employs the user item rating data set. Traditional collaborative filtering approaches compute a similarity value between the target user and each other user by computing the relativity of their ratings, and they only consider the ratings information. User attribute information associated with a user's ...

Recently, collaborative tagging has become popular in the web2.0 world. Tags can be helpful if used for the recommendation since they reflect characteristic content features of the resources. However, there are few researches which introduce tags into the recommendation. This paper proposes a tag-based recommendation framework for scientific literatures which models the user interests with tags...

This paper describes a video browsing tool for media (post-) production, enabling users to efficiently find relevant media items for redundant and sparsely annotated content collections. Users can iteratively cluster the content set by different features, and restrict the content set by selecting a subset of clusters. In addition, similarity search by different features is supported. Desktop an...

Users visit a Yelp business, such as a restaurant, based on its overall rating and often based on other factors like location, hours of location, type/cuisine or other attributes such as free Wifi. In addition to this, users gain useful insight for a Yelp business based on its top reviews and highlights. However, the average rating that a business has, or the top reviews/feedback as per certain...

We present a technique for estimating the similarity between objects such as movies or foods whose proper representation depends on human perception. Our technique combines a modest number of human similarity assessments to infer a pairwise similarity function between the objects. This similarity function captures some human notion of similarity which may be difficult or impossible to automatic...