Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current model-based hybrid recommendation systems typically require extensive feature engineering to constr...

Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current model-based hybrid recommendation systems typically require extensive feature engineering to constr...

Recommendation systems are important big data applications that are used in many business sectors of the global economy. While many users utilize Hadoop-like MapReduce systems to implement recommendation systems, we utilize the highperformance shared-memory MapReduce system Phoenix++ to design a faster recommendation engine. In this paper, we design a distributed out-ofcore recommendation algor...

We present an extension of the Dynamics Based Control (DBC) paradigm to environment models based on Predictive State Representations (PSRs). We show an approximate greedy version of the DBC for PSR model, EMT-PSR, and demonstrate how this algorithm can be applied to solve several control problems. We then provide some classifications and requirements of PSR environment models that are necessary...

Recommendation systems are extensively used to provide a constantly increasing variety of services. Alongside single-user recommendation systems, group recommendation systems have emerged as a method of identifying the items that a set of users will most appreciate collectively. In this thesis, we describe developments in the area of group recommendation techniques and how such techniques can b...

Recommendation systems help users find items of interest. Meta-recommendation systems provide users with personalized control over the combination of recommendation data from multiple information sources. In the process, they provide users with more helpful recommendations by allowing users to indicate how important each parameter is in their decision process, and how data should be weighted du...

With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. How to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similari...

Recommender systems are personalized information systems. However, in many settings, the end-user of the recommendations is not the only party whose needs must be represented in recommendation generation. Incorporating this insight gives rise to the notion of multistakeholder recommendation, in which the interests of multiple parties are represented in recommendation algorithms and evaluation. ...

Personalized recommendation systems can help people to find interesting things and they are widely used in the world. Hybrid peer-to-peer recommendation systems intend to resolve problems of C/S recommendation systems by distributing items and calculating missions to all users. This paper presents our recent research work on the new self-organized personalized recommendation system based on pur...

In this paper, we are going to study about recommendation systems. Recommendation systems are typically used by companies, especially e-commerce companies like Amazon.com, to help users discover items they might not have found by themselves and promote sales to potential customers. A good recommendation system can provide customers with the most relevant products. This is a highly-targeted appr...