The development and evaluation of Information Retrieval and Recommender Systems has traditionally focused on the relevance and accuracy of retrieved documents and recommendations, respectively. However, there is an increasing realization that accuracy ...

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender ...

While microblogging has emerged as an important information sharing and communication platform, it has also become a convenient venue for spammers to overwhelm other users with unwanted content. Currently, spammer detection in microblogging focuses on ...

Context-aware recommendation (CAR) can lead to significant improvements in the relevance of the recommended items by modeling the nuanced ways in which context influences preferences. The dominant approach in context-aware recommendation has been the ...

State-of-the-art methods for product recommendation encounter significant performance drop in categories where a user has no purchase history. This problem needs to be addressed since current online retailers are moving beyond single category and ...

Recommender system has become an important component in modern eCommerce. Recent research on recommender systems has been mainly concentrating on improving the relevance or profitability of individual recommended items. But in reality, users are usually ...

Rating prediction is to predict the preference rating of a user to an item that she has not rated before. Using the business review data from Yelp, in this paper, we study business rating prediction. A business here can be a restaurant, a shopping mall ...

Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, ...

Recommender systems usually need to compare a large number of items before users' most preferred ones can be found This process can be very costly if recommendations are frequently made on large scale datasets. In this paper, a novel hashing algorithm, ...

Existing recommender systems usually model items as static -- unchanging in attributes, description, and features. However, in domains such as mobile apps, a version update may provide substantial changes to an app as updates, reflected by an increment ...

In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for ...

Collaborative filtering with implicit feedbacks has been steadily receiving more attention, since the abundant implicit feedbacks are more easily collected while explicit feedbacks are not necessarily always available. Several recent work address this ...

We study the problem of linking information between different idiomatic usages of the same language, for example, colloquial and formal language. We propose a novel probabilistic topic model called multi-idiomatic LDA (MiLDA). Its modeling principles ...

Social media such as Twitter has come to reflect the reaction of the general public to major events. Since posts are short and noisy, it is hard to extract reliable events based on word frequency. Even though an event term appears in a particularly low ...

The fast growth of technologies has driven the advancement of our society. It is often necessary to quickly grab the evolution of technologies in order to better understand the technology trend. The availability of huge volumes of granted patent ...

Existing work on collaborative filtering (CF) is often based on the overall ratings the items have received. However, in many cases, understanding how a user rates each aspect of an item may reveal more detailed information about her preferences and ...

Recently, some recommendation methods try to relieve the data sparsity problem of Collaborative Filtering by exploiting data from users' multiple types of behaviors. However, most of the exist methods mainly consider to model the correlation between ...

Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show ...

With the development of microblog services, tens of thousands of messages are produced every day and recommending useful messages according to users' interest is recognized as an effective way to overcome the information overload problem. Collaborative ...

Presence of hyperlink in a tweet is a strong indication of tweet being more informative. In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages). By recommending hashtags to ...