We propose two recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three data sets, and we compare the performance of our methods to other recommendation systems recently proposed in the lit...

Information recommendation systems draw attention of practitioners in B-to-C electronic commerce. In an independent recommendation system such as in www.amazon.com, a user cannot compare the recommended item with ones from other information sources. In a brokermediated recommendation system such as in www.dealtime.com, the broker takes the initiative of recommendation, and the information provi...

A product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an e-marketplace. Recommendation methods are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is the most successful recommendation technology but its application to e-commerce has exposed well-known ...

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...

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....

Combining social network information with collaborative filtering recommendation algorithms has helped to alleviate some drawbacks of collaborative filtering, for example, the cold start problem, and has increased the accuracy of recommendations. However, the user coverage of recommendation for social-based recommendation is low as there is often insufficient data about explicit social relation...

Recommendation systems aim to recommend items or packages of items that are likely to be of interest to users. Previous work on recommendation systems has mostly focused on recommending points of interest (POI), to identify and suggest top-k items or packages that meet selection criteria and satisfy compatibility constraints on items in a package, prior work, this paper investigates two issues ...

The wide use of GPS sensors in smart phones encourages people to record their personal trajectories and share them with others in the Internet. A recommendation service is needed to help people process the large quantity of trajectories and select potentially interesting ones. The GPS trace data is a new format of information and few works focus on building user preference profiles on it. In th...

Social recommender systems exploit users’ social relationships to improve recommendation accuracy. Intuitively, a user tends to trust different people regarding with different scenarios. Therefore, one main challenge of social recommendation is to exploit the most appropriate dependencies between users for a given recommendation task. Previous social recommendation methods are usually developed...

Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used by recommender systems in mobile commerce to guarantee the quality of recommendation. This paper proposes a novel multidimensional approach for contextaware recommendation in mobile commerce. The approach represents users, items, context information an...