Dimensionality reduction for data visualization has recently been formulated as an information retrieval task with a well-defined objective function. The formulation was based on preserving similarity relationships defined by a metric in the input space, and explicitly revealed the need for a tradeoff between avoiding false neighbors and missing neighbors on the low-dimensional display. In the ...

We present a new algorithm for recommending alternatives to a given item in an e-commerce setting. Our algorithm is an incremental improvement over an earlier system, which recommends similar items by first assigning the input item to clusters and then selecting best quality items within those clusters. The original algorithm does not consider the recent context and our new algorithm improves t...

A single ‘odd’ interaction can cause two user interaction sessions to diverge in similarity, and stand in the way of generalization. The sensitivity of session-based recommenders to session similarity motivates us to explicitly identify and remove such ‘similarity blockers’. Specifically, we leverage huge amounts of data, which allow us to identify blockers in the form of non-co-occurring items...

With the arrival of the big data era, recommendation system has been a hot technology for enterprises to streamline their sales. Recommendation algorithms for individual users have been extensively studied over the past decade. Most existing recommendation systems also focus on individual user recommendations, however in many daily activities, items are recommended to the groups not one person....

Energy-efficient video distribution systems have become an important tool to deal with the rapid growth in Internet video traffic and to maintain the environmental sustainability of the Internet. Due to the limitations in terms of energy-efficiency of the conventional server centric method for delivering video services to the end users, storing video contents closer to the end users could poten...

CBR systems solve problems by assessing their similarity with already solved problems (cases). Explanation of a CBR system prediction usually consists of showing the user the set of cases that are most similar to the current problem. Examining those retrieved cases the user can then assess whether the prediction is sensible. Using the notion of symbolic similarity, our proposal is to show the u...

In this paper, we investigate the impact of spatial variation on the construction of location-sensitive user profiles. We demonstrate evidence of spatial variation over a collection of Twitter Lists, wherein we find that crowdsourced labels are constrained by distance. For example, that energy in San Francisco is more associated with the green movement, whereas in Houston it is more associated ...

Abstract Collaborative filtering (CF) algorithm is one of the most successful technologies used in personalized recommendation system. However, traditional algorithms focus only on the user ratings and do not take the changes of user interest into account, which affect recommendation quality seriously. To address the issue, this paper proposes a CF algorithm based on interest forgetting curve. ...

Common approaches to creating playlists are to randomly shuffle a collection (e.g. iPod shuffle) or manually select songs. In this paper we present and evaluate heuristics to adapt playlists automatically given a song to start with (seed song) and immediate user feedback. Instead of rich metadata we use audio-based similarity. The user gives feedback by pressing a skip button if the user dislik...