This paper proposes a method of suggesting expanded queries that disambiguate the original Web query which has multiple interpretations. In order to produce a diverse set of queries including those corresponding to infrequent query intents, our method ...

Group recommender systems generate recommendations for a group by aggregating individual members’ preferences and finding items that are liked by most of the members. In this paper we introduce a new approach to preference aggregation and group choice ...

People engage in lifelogging by taking photos with cameras and cellphones anytime anywhere and share the photos, intertwined with captions or descriptions, on social media platforms. The image-text intertwined data provides richer information for image ...

Rank aggregation is a popular problem that combines different ranked lists from various sources (frequently called voters or judges), and generates a single aggregated list with improved ranking of its items. In this context, a portion of the existing ...

Social media has become a platform where users post their messages about a wide range of topics, making it a useful source of information to summarize events such as sports matches. Previous summaries of sports matches generated using social media tended ...

Collaborative filtering generates recommendations tailored to the users’ preferences by exploiting item ratings registered by users. Collaborative filtering algorithms firstly find people that have rated items in a similar fashion; these people are ...

The widespread use of information systems has become a valuable source of semi-structured data. In this context, Entity Resolution (ER) emerges as a fundamental task to integrate multiple knowledge bases or identify similarities between data items (i.e.,...

Online Peer to Peer Lending (P2PL) systems connect lenders and borrowers directly, thereby making it convenient to borrow and lend money without intermediaries such as banks. Many recommendation systems have been developed for lenders to achieve higher ...

Predicting an online user’s future behavior is beneficial for many applications. For example, online retailers may utilize such information to customize the marketing strategy and maximize profit. This paper aims to predict the types of webpages a user ...

This paper proposes an approach on Facebook search in Arabic and English, which exploits several users’ traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (...

Ratings, which are explicit feedback, are the most popular form that is often used in Recommender System (RSs). However, using the actual ratings from neighbors to predict ratings of target user toward target item often leads to low accuracy prediction ...

Social media are playing an increasingly important role in reporting major events happening in the world. However, detecting events and topics of interest from social media is a challenging task due to the huge magnitude of the data and the complex ...

We propose a novel pairwise unified recommendation model (short for pairwise URM). The pairwise URM combines two pairwise ranking-oriented collaborative filtering approaches, namely Collaborative Less-is-More Filtering (CLiMF) and Bayesian Personal ...

Media monitoring services allow their customers, mostly companies, to receive, on a daily basis, a list of documents from mass media that discuss topics relevant to the company. However, media monitoring services often generate these lists by using ...

Personalization plays an important role in many services, just as news does. Many studies have examined news personalization algorithms, but few have considered practical environments. This paper provides algorithms and system architecture for ...

In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine ...

Online advertising is one of the most lucrative forms of advertising, making it an important channel of advertising media. Contextual Advertising is a type of online display advertising that takes cues from the content of the triggering page and ...

Much of the prior research in the recommendations through dialogue separate dialogue and recommendations. However, since the accuracy of the recommendations themselves is not necessarily high, recommendation results rarely meet user needs. However, as ...

The recent boom in social networking services has prompted the research of recommendation systems. The basic assumption behind these works was that "users’ preference is similar to or influenced by their friends". Although many studies have attempted to ...

Exploiting the selections of social friends and foes can efficiently face the data scarcity of user preferences and the cold-start problem. In this paper, we present a Social Deep Pairwise Learning model, namely SDPL. According to the Bayesian Pairwise ...